The background of the EL image of a photovoltaic cell under electroluminescence (EL) presents complex non-uniform texture features, and there are grain pseudo-defects similar to cracks. At the same time, the cracks appear as multi-scale features with various shapes. The above mentioned difficulties have presented great chal-lenges for the detection task. Therefore, this paper proposes a multi-scale Faster-RCNN model that integrates at-tention. On the one hand, an improved feature pyramid network is used to obtain multi-scale advanced semantic feature maps to improve the network's feature expression ability of multi-scale crack defects. On the other hand, an improved attention region proposal network A-RPN is adopted to increase the model's attention to crack defects and suppress the characteristics of complex background and grain pseudo-defects. At the same time, in the RPN network training process, a loss function Focal loss is used to reduce the proportion of simple samples in the training process, so that the model pays more attention to the samples that are difficult to distinguish. Experimental results show that this algorithm improves the accuracy of crack defect detection in EL images, reaching nearly 95%.
Home > Journal Home > Opto-Electronic Engineering
Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
Crack detection based on multi-scale Faster RCNN with attention
Author Affiliations

First published at:Jan 15, 2021
Abstract
References
[1] Anwar S A, Abdullah M Z. Micro-crack detection of multicrystalline solar cells featuring shape analysis and support vector machines[C]//Proceedings of 2012 IEEE International Conference on Control System, Computing and Engineering, 2012: 143‒148.
[2] Su B Y, Chen H Y, Zhu Y F, et al. Classification of manufacturing defects in multicrystalline solar cells with novel feature descriptor[J]. IEEE Trans Instrum Meas, 2019, 68(12): 4675‒4688.
[3] Luo Q W, Sun Y C, Li P C, et al. Generalized completed local binary patterns for time-efficient steel surface defect classification[J]. IEEE Trans Instrum Meas, 2019, 68(3): 667‒679.
[4] Tsai D M, Chang C C, Chao S M. Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion[J]. Image Vis Comput, 2010, 28(3): 491‒501.
[5] Cha Y J, Choi W, Büyüköztürk O. Deep learning‐based crack damage detection using convolutional neural networks[J]. Comput Aided Civ Inf Eng, 2017, 32(5): 361‒378.
[6] Lin H, Li B, Wang X G, et al. Automated defect inspection of LED chip using deep convolutional neural network[J]. J Intell Manuf, 2019, 30(6): 2525‒2534.
[7] Duan K W, Bai S, Xie L X, et al. Centernet: keypoint triplets for object detection[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, 2019: 6568‒6577.
[8] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 2999‒3007.
[9] Girshick R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1440‒1448.
[10] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91‒99.
[11] Cha Y J, Choi W, Suh G, et al. Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types[J]. Comput Aided Civ Inf Eng, 2018, 33(9): 731‒747.
[12] Gao L, Chen N N, Fan Y. Vehicle detection based on fusing multi-scale context convolution features[J]. Opto-Electron Eng, 2019, 46(4): 180331.
高琳, 陈念年, 范勇. 融合多尺度上下文卷积特征的车辆目标检测[J]. 光电工程, 2019, 46(4): 180331.
[13] Liu S, Qi L, Qin H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 8759‒8768.
[14] Corbetta M, Shulman G L. Control of goal-directed and stimulus-driven attention in the brain[J]. Nat Rev Neurosci, 2002, 3(3): 201‒215.
[15] Frazão M, Silva J A, Lobato K, et al. Electroluminescence of silicon solar cells using a consumer grade digital camera[J]. Measurement, 2017, 99: 7‒12.
[16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7132‒7141.
[17] Everingham M, Van Gool L, Williams C K I, et al. The PASCAL visual object classes (VOC) challenge[J]. Int J Comput Vis, 2010, 88(2): 303‒338.
[18] Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3‒19.
Funds:
National Natural Science Foundation of China (61873315)
Export Citations as:
For
Get Citation:
Chen Haiyong, Zhao Peng, Yan Haowei. Crack detection based on multi-scale Faster RCNN with attention[J]. Opto-Electronic Engineering, 2021, 48(1): 200112.