Accurate Detection Algorithm of Multiple Types of Defects in PL Images of Photovoltaic Cells Based on Improved YOLOv5

As the photovoltaic industry develops toward high efficiency and intelligent manufacturing, the requirements for defect detection accuracy and stability in the photovoltaic cell production process continue to increase. To address weak features and large-scale differences of multiple types of defects in photoluminescence images, this study proposes a defect detection method that combines physical consistency preprocessing and improved YOLO based on analyzing the physical characteristics and defect distribution rules of photoluminescence imaging. This method enhances fine-grained defect representation capabilities through full-dimensional dynamic convolution, introduces coordinate convolution to improve spatial positioning sensitivity, and combines a multi-threaded non-local attention mechanism to model the global correlation of cross-regional defects. Experimental results demonstrate that in comparative tests on PV-PL and SCPD datasets (Table 2 ), the method achieves high comprehensive performance in multi-type defect detection tasks, with an F1 score reaching 94.84%. Under conditions where 18% of the PV-PL data set is interference (Fig. 10 ), the false positive rate is approximately 7.0%, and the detection latency is as low as 9.7[Formula: see text]ms. In tests with varying illumination intensities (Table 6 ), the model achieves the lowest average localization error of 1.76[Formula: see text]px under normal lighting conditions. The proposed method has obvious advantages in detection precision and robustness, and can offer effective technical support for photovoltaic cell photoluminescence image defect detection.