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%.
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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
Correspondence: haiyong.chen@hebut.edu.cn
First published at:Jan 15, 2021
Abstract
References
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National Natural Science Foundation of China (61873315)
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Chen Haiyong, Zhao Peng, Yan Haowei. Crack detection based on multi-scale Faster RCNN with attention[J]. Opto-Electronic Engineering, 2021, 48(1): 200112.
