Defect Detection Algorithm for Monocrystalline Silicon Solar Cell Modules Based on Image Processing and Deep Learning

Abstract

In response to the low defect detection accuracy caused by small defect areas and large differences in defect scales in EL images of photovoltaic cell components, a defect detection algorithm for photovoltaic cell modules based on traditional image processing and deep learning is proposed. Firstly, a traditional image processing algorithm is designed to segment the images of the cell modules into individual solar cells for detection. Secondly, the accuracy of defect detection is improved by enhancing the YOLOv8 network. The specific details are as follows: First of all, a dynamic receptive field selection structure called C2DLSK (C2f and Dynamic Large Selective Kernel Module) is designed to replace the C2f module in the backbone. It dynamically selects the appropriate receptive field size for the current target during the feature extraction process to more accurately extract the features of defects. Then the CARAFE (Content-Aware ReAssembly of Features) is used to replace the first nearest-neighbor upsampling module in the neck. At the same time, a bidirectional weighted fusion method called BiConcat is used for feature fusion, which fully utilizes semantic information while enhancing the weight of important features in feature fusion. Finally, the MPDIoU loss function is used to replace the CIoU loss function, further improving the accuracy of defect detection. The experiment shows that under the condition of ensuring real-time detection, the average precision mean average precision (mAP) of this algorithm for defect detection in photovoltaic cell components reaches 85.8%, which is an improvement of 1.9% compared to the original network. Compared with the current mainstream YOLOv3-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv8s, it improves the detection accuracy of photovoltaic cell components by 5.3%, 2.9%, 1.6%, and 0.9% respectively.