Defect detection of solar panels using improved faster R-CNN

Ting Li; Yuan Sun

doi:10.1117/12.2681294

8 June 2023 Defect detection of solar panels using improved faster R-CNN

Ting Li, Yuan Sun

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072H (2023) https://doi.org/10.1117/12.2681294
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Solar panel is an important tool to convert solar energy into electric energy, there are many defects in the production process. The defect area occupies a large size span in the whole image, in view of the above defect characteristics and the detection accuracy of traditional detection methods is not high. To solve the above problems, an improved Faster R-CNN based on bidirectional feature fusion module BiFPN is proposed, the multi-scale defect information is extracted by combining the strong semantic information of the high level feature map and the location information of the low level feature map. The experimental results show that the improved Faster R-CNN model has a detection mAP value of 88% for five kinds of defect samples, which is 11% higher than the traditional Faster R-CNN detection accuracy, which can better meet the needs of industrial practical applications.

Citation Download Citation

Ting Li and Yuan Sun "Defect detection of solar panels using improved faster R-CNN", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072H (8 June 2023); https://doi.org/10.1117/12.2681294

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KEYWORDS

Solar cells

Defect detection

Feature fusion

Object detection

Target detection

Data modeling

Feature extraction

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