Higher accuracy detection strategy for electroluminescent defects in photovoltaic modules based on improved Yolov5

Abstract

Abstract At present, the domestic photovoltaic (PV) industry is developing rapidly. In order to improve the production efficiency of PV cells, a fast and accurate automatic detection model of PV modules’ defects that can be applied in the production line is essential. In this paper, based on the characteristics of significant differences in PV module defect size and a large number of fine defects, an improved defect detection algorithm based on Yolov5 is proposed. Thirteen mainstream defects are divided into two categories according to size, and a series‐connected detection network is constructed for a two‐stage detection. In order to better detect fine defects, this paper proposes the TR‐ResNet module, a residual module composed of the self‐attention, based on the self‐attention mechanism, to replace some fully convolutional residual modules (CNN‐ResNet module) in the Yolov5 backbone network. After testing, the Precision, Recall and mAP of the model are greatly improved and gained 0.904, 0.845, and 0.840. Moreover, the model performs well in stability detection, which can adapt to different production environments and quality requirements. The present study may make the detection work more efficient and improve productivity.

Keywords

• artificial intelligence
• crack detection
• photovoltaic cells