Structure-aware-based crack defect detection for multicrystalline solar cells

Journal Pre-proofs Structure-Aware-based Crack Defect Detection for Multicrystalline Solar Cells Haiyong Chen, Huifang Zhao, Da Han, Weipeng Liu, Peng Chen, Kun Liu PII: DOI: Reference:

S0263-2241(19)31036-X https://doi.org/10.1016/j.measurement.2019.107170 MEASUR 107170

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Measurement

Received Date: Revised Date: Accepted Date:

16 May 2019 12 October 2019 15 October 2019

Please cite this article as: H. Chen, H. Zhao, D. Han, W. Liu, P. Chen, K. Liu, Structure-Aware-based Crack Defect Detection for Multicrystalline Solar Cells, Measurement (2019), doi: https://doi.org/10.1016/j.measurement. 2019.107170

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Structure-Aware-based Crack Defect Detection for Multicrystalline Solar Cells Haiyong Chena,b*, Huifang Zhaoa, Da Hana, Weipeng Liua,b, Peng Chena, Kun Liua a School b State

of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, China

Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China

Abstract: Automatic crack defect detection for multicrystalline solar cells is a challenging task, owing to inhomogeneously textured background, disturbance of crystal grains pseudo defects, and low contrast between crack defect and background. In this paper, a novel structure-aware-based crack defect detection scheme (SACDDS) is proposed. Firstly, the structure features of crack defect and randomly distributed crystal grains are analyzed, and corresponding mathematical models are used to represent two structure features. Secondly, according to Hessian eigenvalues of the above mathematical models, the identification functions of linear-structure and blob-structure are obtained. Then, a novel structure similarity measure (SSM) function is designed by using the identification functions of two structures, which can highlight crack defect, suppress crystal grains simultaneously. It significantly weakens the interference of inhomogeneous texture and obtains uniform background. Further, in order to overcome non-uniform response of crack region and extract crack defect, a tensor voting-based non-maximum suppression (TV-NMS) method is developed. It improves the uniformity of crack defect response and extracts candidate crack defect pixels. Finally, an effective morphological operation is applied to remove non-crack pixels and complete crack defect can be located in the EL images. Experimental results show that the proposed method can completely extract crack defect in the inhomogeneously textured background, which is well effective and outperforms the previous methods. Key words: crack defect detection; Inhomogeneous texture; Hessian eigenvalues; Structure similarity measure; Non-maximum

suppression

1. Introduction

Solar energy is one of the most reliable, clean and no-pollution renewable energy sources, and photovoltaic (PV) power has become a popular way of worldwide sustainable electricity generation [1]. Solar cells are the key part of PV power system, which convert solar energy into electricity. Because the silicon wafer is fragile, crack defect will generate when solar cells are subjected to mechanical stress or thermal stress in fabrication process [2]. Crack defect causes finger interruption and then obstructs collection and transmission of current, which is easy to form hot spots or fragments and functions poorly. In addition, it wastes production materials and damage the stability of PV power system [3].Thus, the quality inspection of solar cells is crucial. Currently, crack defect detection task depends on experienced inspectors. They can identify crack defect from the solar cells images. This way lacks rapidity, reliability, and robustness for all the solar cells. Hence, it is necessary to apply computer vision-based techniques [4] for automatic crack defect detection in solar cells. The computer vision-based defect detection methods are formed from the perspective of image texture features. Texture features can be divided into homogeneous texture and inhomogeneous texture. Specifically, the homogeneous texture refers to a regular texture, which is characterized by self-similarity and repeatability. Conversely, it is inhomogeneous texture. According to the texture feature differences of the object to be detected, current defect detection methods can be classified into three categories including learning-based, homogeneous texture-based and inhomogeneous texture-based methods. Concretely, to extract crack defect features, learning-based methods apply classifiers [5] to perform defect classification. Anwar et al. [6] analyzed crack shape and applied support vector machine (SVM) to implement crack detection. Demant et al. [7] trained a set of labeled crack images to obtain local descriptor, and SVM classifier can identify crack defect. However, in order to ensure detection performance, it is necessary for different classifiers to obtain high-dimensional features. The deep learning-based methods have been widely used in computer vision, which can automatically extract image features. Deep convolutional neural

network (CNN) [8]-[9] was used to learn features from manually labeled image patches and then determine if the collected image has crack defect. However, both the classifiers and the deep learning methods require a large number of labeled images and training samples [10]. Besides, the accurate location of a complete crack in solar cell image with complicated background and high resolution is challenging. Homogeneous texture-based methods mainly process the detection objects with directional or non-directional textures such as cut piece, fabric, wood, liquid crystal display and steel surface, but they all have self-similarity and repeatability. As shown in Fig.1, milling knives and wood grain textures appear as directional repetitive texture. In contrast, steel surface, fabric, pavement, etc. appear as non-directional texture. For the detection object with directional repetitive texture, the Fourier transform method can be used to easily identify defect. In addition, for the non-directional texture detection object such as steel surface [11] and magnetic tile. It can be seen that the defect or flaw are high contrast in the corresponding intensity distribution maps. Each image presents the homogeneously textured background, so it is easy to detect these defects. Specifically, by applying 2D Haar wavelet transform and discrete wavelet transform, crack defect can be extracted in steel surface [12] and asphalt pavement [13]. Li et al. [14] used LBP to describe the local texture features of an image and detect fabric defects. For the defect detection of fabric and liquid crystal display, the Gabor transform [15] contains the global and local features of an image, and it has multi-scale and multi-directional characteristics, which can be used to identify the edge information of defect. Khan et al. [16] proposed a Gabor filter optimal parameter selection method for pavement crack detection. However, the Gabor-based detection algorithm has a high computational complexity and it is difficult to meet the requirement of online detection. Guan et al. [17] proposed an iterative tensor voting method to extract pavement crack, but the background illumination and texture will affect crack segmentation result. Homogeneous texture

Directional repeating texture

wood grain

milling knife

Directionless texture steel

frabic

road

Intensity distribution

Fig.1

Inhomogeneous texture images and corresponding intensity distribution

Inhomogeneous texture-based methods are applied to detect defect in the inhomogeneously textured background, such as multicrystalline solar cells. As shown in Fig.2, the intensity distribution of multicrystalline solar cells show low contrast between crack defect and background. The EL images are characterized by inhomogeneous texture. Compared with the homogeneous texture detection objects mentioned above, defect detection in the inhomogeneously textured background has certain difficulties. Concretely, Tsai et al. [18] applied an anisotropic diffusion scheme that took the gray level and gradient features to adjust the diffusion coefficient. Threshold segmentation [19]-[21] and contrast enhancement [22] were utilized to segment crack defect. However, for these methods, the intensity information is the major consideration, but crystal grains and crack defect have similar intensities in solar cells. Therefore, it is difficult for the complete extraction of crack defect from solar cells. This paper mainly focuses on the crack defect detection for multicrystalline solar cells. Compared with monocrystalline solar cells, multicrystalline solar cells are more common because of lower material and manufacturing costs [23]. However, they consist

of many crystal grains that increase the difficulty of crack defect detection. To observe the crack defects that are invisible to naked eyes, the electroluminescence (EL) imaging technique [24]-[26] is used to capture the near infrared image with a wavelength of 950nm-1250nm. Fig.2(a) shows a defect-free EL image with randomly distributed crystal grains. Fig.2(b) shows a defective EL image with a crack defect. Fig.2(c)-(d) present two dim EL images with low contrast. In order to give a better understanding of our work, three main properties of EL images are listed as follows: 1) There are many crystal grains with random sizes, positions and orientations forming the inhomogeneously textured background in EL images of multicrystalline solar cells. 2) The intensities of crack defect and the randomly distributed crystal grains are similar, which makes low contrast between crack defect and background. 3) Some crystal grains also appearing as linear shapes may be mistaken for crack defects. Inhomogeneous texture

EL images of solar cells

(a)

(b)

(e)

(f)

Intensity distribution

Fig.2

Inhomogeneous texture images: EL images of solar cells and corresponding intensity distribution

Given former analysis, automatic crack defect detection for multicrystalline solar cells is a challenging task. Although the above methods have achieved good performance, most of them are used to detect crack defect in the uniform and homogeneously textured background such as pavement and steel surface. In addition, for solar cells, these methods are performed by either highlighting crack alone or suppressing background alone. They fail to solve the problems of inhomogeneously textured background interference, disturbance of crystal grains pseudo defects, and low contrast between crack defect and background simultaneously. To settle difficulties faced, 2D Hessian enhancement filter is considered as a choice. The eigenvalues of Hessian Matrix [27] can describe and detect different structure information of an image. For example, Hessian-based enhancement filter has been widely used in medical images [28]-[30], which can enhance specific structure such as vessel and suppress non-target structure. Considering crack defect presents linear-structure, which has obvious structure difference from other pixels in the background. In this paper, we propose a novel crack defect detection algorithm based on 2D Hessian enhancement filter. Briefly, the main contributions of the proposed crack defect detection scheme are summarized as follows: 1) A novel structure-aware-based crack defect detection scheme (SACDDS) is proposed, which achieves crack defect detection from the perspective of image structure features. Although the intensities of crack defect and randomly distributed crystal grains are similar in EL images, it can completely extract crack defect. 2) A structure similarity measure (SSM) function is designed to highlight crack defect and suppress crystal grains simultaneously, which significantly weakens the interference of inhomogeneously textured background for multicrystalline solar cells. 3) Not only linear-type crack defect but also high curvature crack defect can be completely extracted, even under the low contrast condition. The remaining part of this paper is organized as follows. Section II presents the structure-aware-based crack defect detection method in detail. Section III gives the experimental results and discussion. The conclusions are given in Section IV.

2. Crack defect detection scheme

This paper presents a novel structure-aware-based crack defect detection scheme (SACDDS) for multicrystalline solar cells. As shown in Fig.3, our method mainly includes two parts: 1) structure similarity measure (SSM) function design; 2) crack defect extraction. Concretely, in the first part, we analyze the structure features of crack defect and crystal grains, and use corresponding mathematical models to represent these structure features. Then, according to Hessian eigenvalues from the above mathematical models, the identification functions of linear-structure and blob-structure are obtained. In order to highlight crack defect and suppress crystal grains simultaneously, we design an initial structure similarity measure (ISSM) function by using the identification functions of two structures. However, there are some spurious responses after ISSM operation in the response map. Further, curvature measure function is added to the ISSM, which can distinguish response of true crack defect from these spurious responses and obtain uniform background. Finally, the structure similarity measure (SSM) function is achieved, which can identify crack defect, and improve the contrast between crack defect and surrounding background. Moreover, it also can remove spurious responses caused by non-crack defect structures and obtain uniform background. Structure similarity measure design Structure feature analysis of crack defect and crystal grains

Structure modeling

Construct identification functions of linear-structure and blobstructure

Initial structure similarity measure (ISSM)

Add curvature measure

Crack defect extraction Improve the uniformity of crack defect response by tensor voting

Structure similarity measure (SSM)

Non-maximum suppression in two symmetrical isosceles triangle regions

Fig.3

Morphological operation

Crack defect extraction and location

Structure-aware-based crack defect detection scheme (SACDDS)

However, the responses of crack defect are non-uniform after SSM processing. In addition, there are still some elongated structures in the response map, which appear similar structure features to crack defect. To address these two problems, a tensor voting-based non-maximum suppression (TV-NMS) method is developed in the second part. Tensor voting is firstly used to improve the uniformity of crack defect response, and then non-maximum suppression is applied to mark the pixel whose intensity is not maximum as zero within a certain local neighborhood. Specifically, the local neighborhood is two symmetrical isosceles triangle regions. Finally, an effective morphological operation is utilized to remove non-crack pixels and complete crack defect can be located in the EL images. In summary, the proposed SACDDS weakens the inhomogeneous texture interference, removes disturbance of crystal grains pseudo defects, and improves the contrast between crack defect and background. 2.1 2D Hessian-based enhancement filter Hessian Matrix has been widely used in image processing, such as edge detection, feature point detection and so on. It can describe the local curvature of a multivariable function. The essence of Hessian-based enhancement filters is to analyze the eigenvalues of Hessian Matrix for each pixel in 2D or 3D images. The Hessian Matrix H(u) of a 2D image 𝑓(𝑢) at a position 𝑢 = (𝑥,𝑦) is given by Eq.(1).

𝐻(𝑢) =

where

∂2𝑓

∂2𝑓

∂2𝑓

[

∂2𝑓 ∂𝑥2 ∂2𝑓 ∂𝑦∂𝑥

][

∂2𝑓 ∂𝑥∂𝑦 ∂2𝑓 2

∂𝑦

]

𝑓𝑥𝑥 𝑓𝑥𝑦 ≜ 𝑓 𝑦𝑥 𝑓𝑦𝑦

(1)

∂2𝑓

， ∂𝑦2， ∂𝑥∂𝑦， ∂𝑦∂𝑥are the second order partial derivatives of 𝑓(𝑢). For each pixel in a 2D image, its 𝐻(𝑢) is a 2D ∂𝑥 2

positive definite matrix, which has two eigenvalues 𝜆1,𝜆2 and corresponding eigenvectors 𝑣1,𝑣2 obtained through eigenvalue decomposition of the Hessian Matrix.

v1

v1

v2 defective EL image

λ1

λ1 λ2

v2

λ2 A

B fxx

fxy

(a)

Fig.4

fyy

(b)

(a) Image anisotropy represented by the eigenvalues and eigenvectors of Hessian matrix . (b) Second-order partial derivative images.

The ellipse formed by eigenvalues 𝜆𝑖(𝑖 = 1,2) and eigenvectors 𝑣𝑖(𝑖 = 1,2) represents anisotropy of image change. Namely, the eigenvalue can describe anisotropy of image change in corresponding eigenvector direction. As shown in Fig.4, there is a blue circle structure ‘A’ and a blue curve structure ‘B’ in Fig.4(a), then corresponding eigenvalues and eigenvectors form two different orange ellipses. In the ellipse of curve structure ‘B’, there is a big difference of two eigenvalues. Namely, 𝜆1 ≠ 𝜆2. It shows the curve structure has larger anisotropy. By comparison, in the circle structure ‘A’, two eigenvalues satisfy 𝜆1 = 𝜆2, so the ellipse of circle structure has stronger isotropy. Table1

Correspondence between eigenvalues of Hessian Matrices and 2D image structure features (H=high, L=low, indicate the eigenvalue values ; +/- indicate the sign of the eigenvalue)

𝜆2

image structure features

𝜆1 -H

-H

+

+

H

H L L

-H + H

blob-structure (high intensity) blob-structure (low intensity) linear-structure (high intensity) linear-structure (low intensity)

Fig.4(b) shows a defective EL image of multicrystalline solar cells and corresponding second order partial derivative images 𝑓𝑥𝑥, 𝑓𝑥𝑦, and 𝑓𝑦𝑦. It can been seen that structure feature of longitudinal crack defect can be presented in 𝑓𝑦𝑦. It follows that Hessian Matrix can detect the structure information of an image. For some point 𝑢 = (𝑥,𝑦) of a 2D image 𝑓(𝑢), corresponding two eigenvalues 𝜆1,𝜆2 satisfy |𝜆1| > |𝜆2|. The relationships between eigenvalues and local image structure features of an image [27]-[28] are shown in Table 1. It shows the eigenvalue of Hessian Matrix reflects the local image structure features information. As shown in Fig.5(a), a red line crosses crack defect in the defective EL image. The corresponding 3D intensity distribution is shown in Fig.5(b). It shows that the crack defect profile presents a Gaussian shape. The Gaussian model can explain the eigenvalues and eigenvectors of Hessian Matrix. It also presents a dark crack defect with low intensity in a bright background with high intensity, which is consistent with the fourth row in Table 1. In addition, the eigenvalue decomposition of Hessian Matrix is to extract the main direction information of an image.

Moreover, the main direction can directly give the direction of the minimum curvature. That is, the direction of the image along the crack defect, which avoids the multiple filtering in multiple directions. Therefore, the eigenvalues of Hessian Matrix including signs and the absolute values are used to detect and then identify specific image structures in this paper.

140

Intensity

130 120 110 100 90 80 70 69 73 77 117 123 129 57 61 65 135 141 147 153 41 45 49 53 Column Row

(a) Fig.5

(b) The profile of crack defect in EL image

In fact, as shown in Eq.(2), the core of Hessian-based enhancement filters is designing a satisfactory response function 𝛷(𝑢) to highlight specific structure and suppress non-target structure. The response value is φ when the structure satisfy the condition ∆. Otherwise, it is non-target structure and its response value is zero.

𝛷(𝑢) =

𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 ∆ 𝑖𝑠 𝑠𝑎𝑡𝑖𝑓𝑖𝑒𝑑， {𝜑, 0， 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(2)

As discussed above, what we need to do is design a specific response function to highlight crack defect and suppress other crystal grains and background information. Hence, it is necessary to analyze the structure feature differences between crack defect and other structures in the background. 2.2 Designing Structure Similarity Measure (SSM) Function The EL images of multicrystalline solar cells can be classified into two structure features including crack defect and background. Whether it is a defective EL image or a defect-free EL image, there are crystal grains randomly distributed in the background. Crystal grains appear as blob-structure and form the inhomogeneously textured background. In contrast, crack defect presents linear-structure feature. Specifically, Fig.6(a) shows a defective EL image and Fig.6(b) shows a defect-free EL image. Fig.6(a1)-(a2) and Fig.6(b1)-(b2) are corresponding 3D intensity distribution and side views. It can be seen that the structure the crystal grains present is likely to be blob-structure. Moreover, crystal grains and crack defect have similar intensities. It is difficult to extract crack defect by relying on intensity features alone. Therefore, in this paper, we distinguish crack defect from randomly distributed crystal grains pseudo defects from the perspective of image structure features.

(b1)

(a1)

(b)

(a)

(a2)

Fig.6

(b2)

Intensity distribution and structure features analysis. (a) defective EL image. (b) defect-free EL image. (a1)-(a2), (b1)-(b2) corresponding three dimensional intensity distribution and side views.

The following work is to design an effective structure similarity measure function to highlight crack defect and suppress crystal grains and background information. Firstly, corresponding mathematical models are used to represent structure features of

crack defect and crystal grains. Then, we construct the identification functions of linear-structure and blob-structure. Considering that we need to highlight crack defect and suppress crystal grains, the initial structure similarity measure function (ISSM) is obtained. Furthermore, the curvature measure is added to suppress background information. Finally, the structure similarity measure (SSM) function is achieved, which can highlight crack defect, improve the contrast between crack defect and surrounding background. Moreover, it can remove spurious responses and obtain uniform background. 2.2.1 Structure Modeling Ideally, we can use a line and a blob-point to represent the structure features of crack defect and crystal grains, respectively. Because of the composition of Hessian Matrix, we should ensure the designed function expressions are continuous and second-order differentiable. Therefore, the function expressions of line 𝑙(𝑥,𝑦) and blob-point 𝑏(𝑥,𝑦)are Eq.(3)-(4), respectively. Specifically, 𝑙(𝑥,𝑦) is a 1D Gaussian function. It represents a line that lies on the 𝑦 axis and parallels to the 𝑥 axis. In addition, 𝑏(𝑥,𝑦) represents a blob-point in the form of 2D Gaussian function.

{ } 𝑏(𝑥,𝑦) = 𝑒𝑥𝑝{ ― } 𝑥2

𝑙(𝑥,𝑦) = 𝑒𝑥𝑝 ― 2𝜎2

𝑥2 + 𝑦2 2𝜎2

(3) (4)

The eigenvalues are computed by: 𝜆1 = 𝐾 + 𝐾2 ― 𝑄2 2

𝜆2 = 𝐾 ― 𝐾 ― 𝑄 where 𝐾 =

2

(5) (6)

(𝑓𝑥𝑥 + 𝑓𝑦𝑦) 2

,𝑄 = 𝑓𝑥𝑥𝑓𝑦𝑦 ― 𝑓𝑥𝑦𝑓𝑦𝑥.

The eigenvalues of linear-structure and blob-structure meet the condition Eq.(7) and Eq.(8), respectively.

𝜆1 =

―1 𝜎2

< 0, 𝜆2 = 0

𝜆1 = 𝜆2 =

―1 𝜎2

<0

(7) (8)

|𝜆2|

In order to distinguish linear-structure and blob-structure, let 𝑒2 = |𝜆1| in this paper. Then, linear-structure satisfies 𝑒2 = 0 and blob-structure satisfies 𝑒2 = 1. So, 𝑒2 is used as a basis for determining whether some pixel belongs to linear-structure or blob-structure. Further, similarity measure function 𝑆𝑖(𝑖 = 𝑙 𝑜𝑟 𝑏) are built to identify different structure features, as shown in Eq.(9) and Eq.(10), respectively.

𝑆𝑙(𝜆1,𝜆2) = 1 ― 𝑒2 =

|𝜆1| ― |𝜆2| |𝜆1|

|𝜆2|

𝑆𝑏(𝜆1,𝜆2) = 𝑒2 = |𝜆1|

(9) (10)

From Eq.(9)-(10), it can been seen that for the specific structure feature, each similarity measure function 𝑆𝑖 has an output value of 1, and for the other structure feature, the output is 0. In other words, if the pixel to be detected belongs to blob-structure, 𝑆𝑏 is 1 and 𝑆𝑙 is 0. Similarly, if the pixel to be detected belongs to linear-structure, 𝑆𝑙 is 1 and 𝑆𝑏 is 0. Therefore, we can correctly determine the structure feature of a pixel based on the similarity function 𝑆𝑖. In addition, there are differences between the absolute values of eigenvalues of linear-structure and blob-structure. So, the absolute values 𝑀𝑖(𝑖 = 𝑙 𝑜𝑟 𝑏) of eigenvalues can be used as an additional discriminant condition, which satisfies Eq.(11) and Eq.(12), respectively.

𝑀𝑙(𝜆1,𝜆2) = |𝜆1|

(11)

𝑀𝑏(𝜆1,𝜆2) = |𝜆2|

(12)

According to Eq.(7)-(8), |𝜆2| can well distinguish linear-structure and blob-structure. |𝜆2| will be greater than 0 if a pixel

belongs to blob-structure. Similarly, if a pixel belongs to linear-structure, |𝜆2| is 0. It shows that for a specific structure, neither of 𝑀𝑖 functions outputs a response that is in favor of the other structure. Based on the above discussion, the similarity measure function 𝑆𝑖 combines with the absolute values of eigenvalues function 𝑀𝑖 can obtain the linear-structure identification function 𝛷𝑙 and the blob-structure identification function 𝛷𝑏.

|𝜆 | ― |𝜆2|, 𝜆1 < 0 𝛷𝑙(𝑢) = 0,1 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

{

𝛷𝑏(𝑢) =

{

|𝜆2|2 |𝜆1| ,

0,

(13)

𝜆1 < 0,𝜆2 < 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(14)

linear-structure identification

defective EL image

a

b (b)

(b1)

blob-structure identification (a)

(c)

Fig.7

(c1)

Performance analysis of identification function. (a) defective EL image. (b)-(b1) response map of linear-structure identification and corresponding response value distribution. (c)-(c1) response map of blob-structure identification and corresponding response value distribution.

In this paper, we use two qualitative indices to evaluate the identification performance of designed identification functions. Namely, sensitivity and specificity. Specifically, the sensitivity indicates how sensitive the designed identification function to some structure feature. Namely, when identifying the blob-structure, the designed blob-structure identification function should output a larger response value than other structures. Similarly, the specificity measures how specific the designed identification function to some structure feature. That’s to say, when acting on the linear structure, a highly specific blob-structure identification function does not output a large response value. Fig.7(a) is a defective EL image with randomly distributed crystal grains, in which ‘a’ and ‘b’ represent crystal grains and crack defect, respectively. Fig.7(b)-(b1) show the response map of linear-structure identification and response value distribution. It shows that crack defect is effectively identified, and its response value is significantly higher than other crystal grains. Fig.7(c)-(c1) show the response map of blob-structure identification and response value distribution. It shows that crystal grains have a larger response value and the crack defect is not effectively be identified. Thus, the above experimental results indicate both linear-structure identification function 𝛷𝑙 and the blob-structure identification function 𝛷𝑏 have high sensitivity and specificity. 2.2.2 Initial Structure Similarity Measure In the crack defect detection task of multicrystalline solar cells, the linear crack defect structure should be highlighted and the crystal grains structure should be suppressed. Therefore, integrating the linear-structure identification function 𝛷𝑙 and the blob-structure identification function 𝛷𝑏 can obtain the initial response function 𝜙(𝑢) mentioned in Section 2.1, namely, initial

structure similarity measure (ISSM) 𝜙0(𝑢), which measures the likelihood that a crack defect is present.

𝜙0(𝑢) =

{

(

(|𝜆1| ― |𝜆2|)𝑒𝑥𝑝 ― 0,

|𝜆2|2 |𝜆1|

),

𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(15)

𝜆1 < 0

Because the Hessian Matrix is composed of the second derivatives, they are sensitive to noise. Gaussian filter is performed to reduce the noise of the image. Fig.8(a)-(b) show two defective EL images, and Fig.8(c)-(d) are the filter results by Gaussian filter. Fig.8(e)-(f) are the response maps obtained by ISSM and Fig.8(g)-(h) present corresponding 3D intensity distributions. The ISSM response map shows that the response value of crack defect is larger than the response value of other structures in the background, and crack defect is well highlighted. However, there are some spurious responses in the background. It is necessary to add other constraint conditions to remove these spurious responses caused by non-crack defect structures. linear-structure identification function blob-structure identification function

structure similarity } initialmeasure (ISSM) crack response

spurious response

Gaussian filter

(a)

(e)

(c)

crack response

(g)

spurious response

Gaussian filter

(b)

(d) (f)

Fig.8

(h)

ISSM result analysis. (a)-(b) defective EL images. (c)-(d) Gaussian filter results. (e)-(f) ISSM results. (g)-(h) response value distributions after ISSM.

2.2.3 Structure Similarity Measure Because the intensity distribution of background is relatively uniform, the curvature change in any directions is small. When calculating the Hessian Matrix, the second order differential value is small, and the eigenvalue of the background pixel is small. So, curvature measure function 𝑚 can be used to distinguish background pixels. Further, inspired by the Frangi’s filter [27], we design the function 𝑛 to suppress background information, which can remove the spurious responses in the ISSM response map.

𝑚 = 𝜆12 + 𝜆22

(16)

(

𝑚

)

(17)

𝑛 = 1 ― 𝑒𝑥𝑝 ― 2𝑎2

Finally, the structure similarity measure (SSM) function 𝜙(𝑢) is obtained, which corresponds to Eq.(2). In particular, there is only an adjustable constant parameter 𝑎 in the SSM.

𝜙(𝑢) =

{

(

(|𝜆1| ― |𝜆2|)𝑒𝑥𝑝 ― 0,

|𝜆2|2 |𝜆1|

)(1 ― 𝑒𝑥𝑝 ( ― )), 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑚

2𝑎2

(18)

𝜆1 < 0

As shown in Fig.9, Fig.9(i)-(j) are the response maps obtained by SSM and Fig.9(k)-(l) present corresponding 3D intensity distributions. The SSM operation highlights crack defect, and improves the contrast between crack defect and surrounding

background. Moreover, it can remove spurious responses and obtain uniform background, which significantly weakens the inhomogeneously textured background. It is helpful for further extraction of crack defect. structure similarity measure (SSM) crack response

+curvature measure= (k)

(i)

crack response

(l)

(j) Fig.9

(i)-(j) SSM results. (k)-(l) response value distributions after SSM.

2.3 Crack Defect Extraction by Tensor Voting-based Non-Maximum Suppression (TV-NMS) Tensor voting [19] can use the second order tensor to extract the space geometric features. Specifically, the matrix eigenvalues reflect the probability that a pixel may be a certain geometry structure, while the eigenvectors indicate the tangential and normal relationship of the corresponding structure. In this paper, tensor voting is used to improve the uniformity of crack defect. As shown in Fig.10(a)-(b), crack defect in the response map after SSM processing is sufficiently obvious, but the response values of these crack pixels are non-uniform. Fig.10(c) shows the stick tensor and ball tensor. Fig.10(d)-(e) present the crack probability map after the tensor voting processing, the uniformity of response values is improved. The results show that the orientation of all crack pixel points is consistent. stick tensor

(d)

(a)

ball tensor (b)

Fig.10

(c)

(e)

Crack defect extraction by TV-NMS. (a)-(b) Crack response maps. (c) Stick tensor and ball tensor. (d)-(e) Uniformity improvement by tensor voting.

After improving the uniformity of crack defect response, we use the non-maximum suppression (NMS) method to extract the crack defect points. Concretely, it is applied to mark the pixel whose intensity is not maximum as zero within a certain local neighborhood. Concretely, the local neighborhood are two symmetrical isosceles triangle regions in Fig.11(a), which ensures that

all the candidate crack pixels can be marked. Specifically, 𝛽 is the angle of the isosceles triangle, 𝑟 is the side of the isosceles. 𝑒 is the tangential direction at crack point. Fig.11(b)-(c) shows the result of the NMS, in which complete crack defect points are extracted. For some local maximum points of non-crack pixels, an effective morphological operation is applied, and the crack defect pixels are shown in Fig.11(d)-(e). The complete crack defect can be located in the EL image, as shown in Fig.11(f)-(g).

β

e r

Morphological operation

(b)

(d)

Crack location

(f)

Non-maximum suppression in two symmetrical isosceles triangle regions (a) (c)

Fig.11

(e)

(g)

(a) Non-maximum suppression. (b)-(c) Candidate crack defect pixels. (d)-(e) Crack defect pixels after morphological operation. (f)-(g) Crack location.

3. Experimental result and discussion

As shown in Fig.12, the crack defect detection system includes EL images acquisition unit, electrode holder and motion unit, and transmission unit. To avoid the influence of ambient light, EL images are obtained in a darkroom. Concretely, EL images acquisition unit is composed of the near infrared Mono Chrome camera of STC-SBS500POE with resolution of 5.0 million (2448 × 2048) and Satoo industrial lens of VTG1214-M4. In addition, all experiments are executed on the T1580 workstation, which includes a high-performance computation server containing 3.5 GHz processor with 32-GB memory to ensure real-time online crack defect detection. EL images acquisition

Darkroom Near infrared camera and Lens Crack defect detection

Solar cell

Dispaly

Probe Copper electrode

(a)

Fig.12

(b)

Crack defect detection system. (a) Procedure of crack defect detection. (b) Parts of the hardware.

3.1 Data Set establishment and Parameter Setting The data set used in this paper is composed of more than 10,000 EL images captured from the production line in Tianjin Yingli new energy Co., Ltd. 1,000 images to be tested are randomly selected from the EL images data set to verify the performance of the proposed structure-aware-based crack defect detection algorithm. In terms of parameter setting, 1) in the part of structure similarity measure design, the curvature measure 𝑚 function plays a

key role in the response value of crack defect. That is, the only adjustable parameter 𝑎 affects the smoothness of the response map. To select the optimal parameter 𝑎, a detailed proving experiment is presented. To illustrate the rationality of parameter changes, the first explanation is that the parameter 𝑎 is calculated by Eq.(19) in the algorithm, and the parameter 𝑞 is a manually set constant. Therefore, we can gradually change the parameter 𝑞 to adjust parameter 𝑎. 𝑎 = 2𝑞2

q=15

(19)

0

0.0673

0.7315

0.2039

0

0

0.0507

0.5770

0.1489

0

0

0.0835

0.7160

0.1918

0

0

0.0930

0.7889

0.1925

0

0

0.0871

0.8665

0.2082

0

q=10

0

0.1487

1.5290

0.4434

0

0

0.1122

1.2096

0.3244

0

0

0.1848

1.5041

0.4194

0

0

0.2056

1.6509

0.4210

0

0

0.1927

1.8076

0.4548

0

q=5

(a)

0

0.5404

4.2466

1.4880

0

0

0.4103

3.4049

1.0996

0

0

0.6770

4.2717

1.4453

0

0

0.7482

4.6094

1.4519

0

0

0.7035

4.9756

1.5591

0

q=1

0

2.3357

6.3736

3.8176

0

0

1.8770

5.2348

2.9600

0

0

3.1614

6.6774

4.2534

0

0

3.2743

6.9845

4.2910

0

0

3.1725

7.3581

4.4545

0

(a1)

q=15

0.8209

1.7036

0.2492

0

0.3965

2.0212

0.5121

0

0

0.1561

2.0584

0.9633

0.0010

0

0.0406

1.3748

1.0706

0.0229

0

0.0261

0.6582

1.1648

0.0876

q=10

(b)

0

0

0

0

2.6082

4.7348

0.9011

0

1.3571

5.3568

1.7153

0

0

0.5752

5.5054

2.9911

0.0041

0

0.1573

3.9076

3.1558

0.0894

0

0.1019

2.0712

3.4521

0.3298

q=5

0

0

0.3816

0.8149

0.1129

0

0.1815

0.9773

0.2355

0

0

0.0704

0.9932

0.4499

0.0004

0

0.0182

0.6543

0.5056

0.0102

0

0.0117

0.3066

0.5494

0.0394

q=1

0

5.4023

7.1172

3.7116

0

3.8468

7.4747

4.3640

0

0

2.8596

7.7844

5.7685

0.0921

0

1.6772

6.1182

5.3271

1.1390

0

1.2520

4.1602

5.9001

2.1525

0

(b1)

Fig.13

Effect of the only parameter 𝑎 in the designed SSM function. (a)-(b) defective EL image. (a1)-(b1) corresponding response map and response value distribution of the crack region labeled as yellow in different parameters.

As shown in Fig.13, different parameters 𝑞 are selected, which can reflect the change of parameter 𝑎. Concretely, Fig.13(a)-(b) show two defective EL images, and Fig.13(a1)-(b1) present the corresponding response map and response value distribution in different parameter 𝑞. Specifically, the tables are the corresponding response value distributions of crack regions labeled as yellow in Fig.13(a)-(b). Firstly, we select the optimal parameter 𝑞 according to the response map. From the response maps in Fig.13(a1)-(b1), it can been seen that if 𝑞 is small, i.e. 𝑎 is small, the effect of 𝑚 will be magnified, and the response value by SSM fluctuates greatly. For example, there are many spurious responses in the response map when 𝑞 is 1. On the contrary, if 𝑞 is large, i.e. 𝑎 is large,

it will have a certain inhibition on the change of 𝑚, and the response map will be relatively smooth. When 𝑞 is 15, the response map has a uniform background. In short, a more uniform background can be obtained when the parameter 𝑞 varies from 5 to 10. Secondly, the response value distribution also should be considered. The aim of SSM is to not only suppress other no-crack structures to obtain uniform background, but also highlight crack defect and improve the contrast between crack and background. However, there is response loss in crack region after SSM processing, which can been seen from the tables in Fig.13(a1)-(b1). Specially, in the table entry orange coloured in Fig.13(a1), the corresponding response value is 2.3357 when 𝑞 is 1. When 𝑞 is 15, the corresponding response value is 0.0673. With the increase of parameter 𝑞, the response values to crack defect are decreasing. Thus, the parameter 𝑞 cannot be infinite. After comprehensive consideration, in order to both highlight crack defect and get uniform background, when 𝑞 is set to 5, namely, the parameter 𝑎 is 50, the performance of the whole detection algorithm is optimal. In this case, the SSM function can highlight crack defect, suppress crystal grains simultaneously and obtain uniform background. 2) Because the SSM operation provides a sufficient preparation for the subsequent crack defect extraction. Besides, the tensor voting is applied to improve the uniformity of crack defect response. In the tensor voting process, the specific parameter settings are as follows: the voting scale is set to 5. The stick saliency threshold is 60%, which can remove the small saliency structure and improve the processing speed of the voting algorithm. 3) It can be observed that the width of crack defect is between 3 and 5 pixels after analyzing a large number of images, which also can been seen from the above figures in Fig.13(a1)-(b1). The response values in crack region are greater than 0, and the response values of background are zero. Therefore, in the TV-NMS process, the radius 𝑟 is set to 10, which ensures all the crack defect pixels can be extracted in the local neighborhood. The angular range 𝛽 of the symmetrical isosceles triangle is set to 𝜋 6. 3.2. Performance Evaluation

3.2.1 Qualitative evaluation Our method is compared with Tsai’s [19] method (AD method) and Chiou’s [21] method (LT method) on the same data set to demonstrate the effectiveness, since both AD and LT method are applied to detect crack defect on solar cells. To verify the effectiveness further, our method is then compared with Medina’s [16] method (Gabor method), Guan’s [18] method (ITV method), which were proposed to detect crack on concrete and pavement surfaces. Fig.14(a)-(f) give defective EL images with randomly distributed crystal grains and low contrast. Fig.14(a1)-(f5) show the corresponding crack defect detection results by different methods, in which the detected defects are labeled as red. Fig.14(a6)-(f6) present ground truth images, in which the crack defects are labeled as white. The AD method applied an anisotropic diffusion scheme that took the gray level and gradient features to adjust the diffusion coefficient. In order to optimal detection performance, the diffusion iteration 𝑇 is set to 3, normalized parameter 𝐾 is set to 4, threshold constant 𝐶 is set to 3, and morphological operation is also applied. In Fig.14(c1), crystal grains with low gray level and high gradient are mistaken for crack defect. The LT method used a local threshold-based algorithm to segment crack defect. It mainly partitions an image into several sub-images and calculates each sub-image’s mean and standard deviation. The detection results are shown in Fig.14(a2)-(f2), and the crystal grains are detected as crack defect due to only take gray level information into account. In addition, the crack defect cannot be detected in the dim EL images, such as in Fig.14(d2). The Gabor method applied the Gabor filter invariant to rotation for concrete tunnels, which can detect crack in any directions. This method used the differential evolution algorithm to obtain Gabor filter parameters, and the maximum response was selected as the enhancement result. However, the concrete tunnel surface is uniform and high contrast between crack and background. As shown in Fig.14(a3)-(c3), the Gabor filter-based method mistakes crystal grains as crack defect. For low contrast EL images in Fig.14(d3)-(e3), it is unable to detect crack defect effectively. The ITV method utilized iterative tensor voting (ITV) for pavement crack extraction, which can well extract crack in uniform

pavement surface. However, it involved multiple voting process, and the computational time is prerequisite. As shown in Fig.14(a4)-(b4), ITV can well extract crack defect in EL image of solar cells. However, it cannot accurately extract the presence of strong crystal grains disturbance and crack defect in low contrast condition, as shown in Fig.14(d4). Compared with the above four methods, our method can extract the crack defect more completely and accurately, and corresponding extract results are shown in Fig.14(a5)-(f5). Moreover, the detailed advantages are summarized as follows. 1) When compared with AD method and LT method, it can extract complete crack defect and weaken the disturbance of crystal grains with low gray level and high gradient. 2) Compared with Gabor method, our method can eliminate the false detection caused by random crystal grains and still extract the crack defect in the case of low contrast. 3) Compared with ITV method, by applying SSM, it can ensure the effectiveness of tensor voting and less calculation.

(a1)

(a2)

(a3)

(a4)

(a5)

(a6)

(b)

(b1)

(b2)

(b3)

(b4)

(b5)

(b6)

(c)

(c1)

(c2)

(c3)

(c4)

(c5)

(c6)

(d)

(d1)

(d2)

(d3)

(d4)

(d5)

(d6)

(e)

(e1)

(e2)

(e3)

(e4)

(e5)

(e6)

(f) EL images

(f1) AD method

(f2) LT method

(f3) Gabor method

(f4) ITV method

(a)

Fig.14

(f5) Our method

(f6) Ground truth

Detection results using the four methods. (a)-(f) EL images (a1)-(f1), (a2)-(f2), (a3)-(f3) and (a4)-(f4) corresponding detection results using Gabor method, AD method, ITV method and our method.

3.2.2 Quantitative evaluation

Table 2

Performance evaluation indices

Indices

Meaning

Lg

the number of crack defect pixels in ground truth image

Lt

the number of pixels extracted by detection algorithm the matched number of pixels between 𝐿𝑡 and 𝐿𝑔 cpt = L L

L cpt

g

crt

crt = L Lt

F-measure

F ― measure = (2 × cpt × crt)/(cpt + crt)

To quantitatively evaluate the performance of different methods, three indicators [6] including completeness (cpt), correctness (crt) and F-measure are used in this paper. As shown in Table 2, 𝐿𝑔 is the number of crack defect pixels in the corresponding ground truth image. 𝐿𝑡 is the total number of extracted pixels in the detection result. 𝐿 is the number of pixels in the detection result which matches the 𝐿𝑔 in ground truth image. The cpt index indicates the completeness of the detection result. If 𝐿 = 𝐿𝑔, cpt will be 1, which presents the perfect match between the number of crack defect pixels extracted by the detection algorithm and the ground truth image. Conversely, cpt is 0. The crt index measures the correctness of the detection results. In fact, there are non-crack pixels that are also extracted, so the condition 𝐿 ≤ 𝐿𝑡 is satisfied, and then the value of crt is between 0 and 1. The F-measure index can comprehensively evaluate the performance of detection algorithm, which represents the weighted harmonic average of the crt and cpt indicators. The higher the value, the more effective the detection algorithm is. Table 3

Experimental results of five methods.

Methods

cpt

crt

F-measure

Average time(ms)

AD method

85.6

90.1

87.8

72

LT method

81.5

80.7

81.1

69

Gabor method

80.3

82.5

81.4

84

ITV method

89.2

87.4

88.3

106

Our method

94.1

95.9

94.9

53

Based on the EL images with defective pixels labeled manually in advance, the three indicators are given in Table 3. The experimental evaluation results of different methods are intuitively shown in Fig.15. The Gabor method and the ITV method were proposed to detect crack in pavement, which has relatively uniform background and high contrast. Therefore, for EL images with inhomogeneously textured background, the above two methods are unable to remove the crystal grains disturbance, so they provide relatively low cpt and crt. The AD and LT methods were applied to detect crack defect in solar cells, which used the gray level and gradient features. Nevertheless, many crystal grains have such features, so it makes many false detection. The proposed SSM can fundamentally weaken the inhomogeneous texture interference. Further, it makes a good preparation for subsequent tensor voting. Therefore, cpt, crt and F-measure are higher than other methods. 100 cpt

evaluation indices values

95

crt

F-measure

90 85 80 75 70 65 60

AD method

Fig.15

LT method

Gabor method

ITV method

SSM method

The evaluation indices values of different methods.

Table 4

Experimental results of defect-free EL images.

Defect-fr ee EL

AD method

LT method

Gabor method

ITV method

Our method

images 300

Defect-fr

Defectiv

Defect-fr

Defectiv

Defect-fr

Defectiv

Defect-fr

Defectiv

Defect-fr

Defectiv

ee

e

ee

e

ee

e

ee

e

ee

e

211

89

231

69

247

53

261

39

294

6

In terms of the computational efficiency, the average processing time for each test EL image with 125 × 125 pixel size of different methods is given in the fifth column of Table 3. The parameters of all methods are the same as the aforementioned. It shows the average time of our method is superior to that of other methods. In addition, 300 defect-free EL images with random texture are selected to verify the robustness of different methods. The specific experimental results are shown in Table 4. For the detection accuracy of the defect-free EL images, it can be seen that the proposed SACDDS is superior to other methods 3.3 Supplementary analysis

3.3.1 Identification performance analysis of SSM To explain the effectiveness of the designed SSM, we make an experiment by comparing the basic tensor voting results. Fig.16(a)-(b) show two defective EL images. Fig.16(a1)-(b1) are the tensor voting results directly applied on the defective EL images, which show the orientation information is messy. It mainly caused by the randomly distributed crystal grains in the background, leading to the inaccurate voting. In contrast, in Fig.16(a2)-(b2), after the SSM operation, the tensor voting results can clearly reflect the primary structure and orientation information of crack defect. It strongly proved that the effectiveness of the designed SSM for the crack defect detection in EL images of solar cells.

Fig.16

(a)

(a1)

(a2)

(b)

(b1)

(b2)

(a)-(b) defective EL images. (a1)-(b1) the results of tensor voting without SSM. (a2)-(b2) the results tensor voting with SSM.

3.3.2 Comparison with Frangi’s filter

(a1)

(a)

(b3)

(b)

(a2)

Fig.17

(b1)

(a3)

(b2) (a4)

(b4)

Comparison of response maps between Frangi’s filter and SSM. (a)-(b) defective EL images. (a1)-(b1) response maps obtained by Frangi’s filter. (a2)-(b2) response maps obtained by SSM.

To verify the performance of the proposed SSM, we make a comparison of the response map between Frangi’s filter [27] with SSM. Frangi’s filter is a classic Hessian-based enhancement filter method. It uses all the Hessian eigenvalues to enhance vessel structure and suppress noise and background simultaneously. The corresponding response function is shown in Eq.(17).

𝜙(𝑢) =

{

𝑅𝛽2

(

(

𝑠2

))

𝑒𝑥𝑝 ( ― 2𝛽2) 1 ― 𝑒𝑥𝑝 ― 2𝑐2 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 0,

(17)

𝜆1 > 0

2 2 where 𝑅𝛽 = 𝜆2 𝜆1, 𝑠 = 𝜆1 + 𝜆2 , 𝛽 and 𝑐 are manually set constants.

Fig.17(a)-(b) show two defective EL images. Fig.17(a1) and (b1) present the response maps obtained by Frangi’s filter in a single scale. Fig.17(a2) and (b2) shows the corresponding intensity distribution of red frame in Fig.17(a). It can be observed that crack defect is salient in the corresponding response map. However, there are many spurious responses caused by crystal grains in the background. In contrast, Fig.17(a3) and (b3) are the response maps by the SSM operation. Fig.17(a4) and (b4) are the corresponding intensity distribution of red frame in Fig.17(b). As shown in Fig.17(b4), some spurious responses are removed by the SSM, which can not only highlight crack defect but also get more uniform background. It is beneficial to the subsequent crack defect extraction. 3.3.3 Robust analysis As shown in Fig.18(a)-(c), the proposed SACDDS is also applied on dark EL defective images, which show low contrast between crack defect and background. Fig.18(a1)-(c1) are the crack defect detection results. It can been seen that the crack defect can be completely extracted. Thus, the proposed SACDDS is robust to brightness changes.

(a)

(b)

(c)

(a1)

(b1)

(c1)

Fig.18

Robustness of the SACDDS

3.3.4 Other applications

(a)

Fig.19

(b)

(c)

(d)

(e)

(f)

Other textured surfaces with linear-type defect and detection results by our method. (a) pavement images with crack defect. (d) steel images with scratch defect. (b) and (e) corresponding response maps obtained by SSM. (c) and (f) corresponding detection results.

The proposed SACDDS also can be applied to other textured surface images with linear-type defects. Fig.19(a) and (d) show two pavement images with crack defect and two steel surface images with scratch defect, respectively. Concretely, the pavement images appear as uniform background without pseudo defects, and the crack defects are salient. In contrast, the steel surface

images present homogeneously textured background, but they show low contrast between the scratch defects and the background. The corresponding response maps obtained by SSM are shown in Fig.19(b) and (e). Fig.19(c) and (f) show the corresponding detection results of our method, in which the crack and scratch are labeled as red. The defect extraction results show that the proposed method can achieve satisfactory performance.

IV. Conclusions

In this paper, a novel structure-aware-based crack defect detection scheme (SACDDS) was proposed, which can extract complete crack defect accurately as well as efficiently in the complex inhomogeneously textured background. A structure similarity measure (SSM) function was designed to aware the crack defect structure and suppress random crystal grains simultaneously, which weakens the inhomogeneous texture background. Then, a tensor voting-based non-maximum suppression (TV-NMS) method was developed to extract candidate crack defect pixels. Especially, the experimental results have shown that the proposed SSM function has a stronger power of highlighting crack defect and suppressing randomly distributed crystal grains, thus providing a highly efficacious preparation for further crack defect extraction. It is promising to extend SSM to apply as a prior operation in deep learning algorithms to provide clear crack defect identification features. Besides, the proposed method achieves good performance on other textured surface images with linear-type defects. However, the only parameter 𝑎 in the designed SSM is determined by evaluating a number of test images. The future work is the automatic selection of parameter 𝑎.

Acknowledgments

This work was supported in part by National Natural Science Foundation (NNSF) of China under Grant 61873315, Natural Science Foundation of Hebei Province under Grant F2018202078, Science and Technology Program of Hebei Province under Grant 17211804D, Hebei Province Outstanding Youth Science Fund (F2017202062) and Talent Support Project in Hebei Province.

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International Conference on Image Processing. IEEE, 2010. Highlights 

A novel structure-aware-based crack defect detection scheme (SACDDS) is proposed

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A structure similarity measure (SSM) function is designed

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The SSM can weaken the interference of inhomogeneously textured background

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The scheme can detect linear-type and high curvature crack defect

Declaration of Interest Statement The authors have declared that they have no conflict of interest. Conflict of Interest Form The authors have declared that they have no conflict of interest.