Defect Detection of Photovoltaic Modules Based on Electroluminescence Images: A RCLM-YOLO Method

In this article, a solar cell defect detection method named RCLM-YOLO, an improved YOLOv8n algorithm is proposed for the characteristics of complex solar cell image backgrounds, diverse defect morphologies, and stringent real-time detection requirements. Firstly, the backbone network of YOLOv8 is redesigned using a decoupling approach, employing reversible connections to progressively separate low-level texture details from high-level feature information. Secondly, a Local Window Attention mechanism is introduced at the low-level layers to suppress background noise, while the SimAM module is integrated at the high-level layers to enhance the discrimination ability of high-level features. Finally, an Inner-CIoU auxiliary regression box is adopted to improve the learning capability of the model for medium-quality samples. Experimental results show that RCLM-YOLO achieves an improvement of 3.0% and 4.4% in [email protected] and [email protected]:0.95, respectively, on the PVEL-AD dataset compared to YOLOv8n, reaching 92.7%. Additionally, the parameter count and computational cost are reduced by 25.6% and 15.9%, respectively.