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19 November 2021 Improved faster R-CNN algorithm for defect detection of electromagnetic luminescence
Yucheng Tao, Zhenying Xu, Qinghua Liu, Linhang Li, Yuxuan Zhang
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Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 1205923 (2021) https://doi.org/10.1117/12.2617320
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China
Abstract
Defect detection methods for photovoltaic (PV) devices based on electroluminescence (EL) imaging technology are crucial to maintain productivity and prolong components’ life. However, due to the lack of feature extraction capability for morphologically complex defects and some small defects, the traditional object detection algorithms perform not well in EL defect detection. Therefore, an improved algorithm based on the Faster Region-based Convolutional Network algorithm (Faster R-CNN) is proposed to improve the detection performance of multi-scale defects. Specifically, an improved residual module based on deformable convolution and attention module is proposed to improve the detection rate of morphologically complex defects. And a Feature Pyramid Network (FPN) is utilized in the proposed algorithm to improve the detection performance of small defects. In addition, the GIOU loss, instead of the original smooth L1 loss, is utilized to improve the boundary box regression accuracy. Experimental results on the detection of the EL defects show the high efficiency of the proposed algorithm. The method is expected to provide more guiding feedback in both practical design and reliable diagnosis of the PV industry.
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Yucheng Tao, Zhenying Xu, Qinghua Liu, Linhang Li, and Yuxuan Zhang "Improved faster R-CNN algorithm for defect detection of electromagnetic luminescence", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 1205923 (19 November 2021); https://doi.org/10.1117/12.2617320
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KEYWORDS
Electroluminescence
Defect detection
Detection and tracking algorithms
Convolution
Feature extraction
Solar cells
Photovoltaics
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