ST-YOLO: A defect detection method for photovoltaic modules based on infrared thermal imaging and machine vision technology

Photovoltaic panels are the core components of photovoltaic power generation systems, and their quality directly affects power generation efficiency and circuit safety. To address the shortcomings of existing photovoltaic defect detection technologies, such as high labor costs, large workloads, high sensor failure rates, low reliability, high false alarm rates, high network demands, and slow detection speeds of traditional algorithms, we propose an algorithm named ST-YOLO specifically for photovoltaic module defect detection. This algorithm is based on YOLOv8s. First, it introduces the C2f-SCconv convolution module, which is based on SCconv convolution. This module reduces the computational burden of model parameters and improves detection speed through lightweight design. Additionally, the Triplet Attention mechanism is incorporated, significantly enhancing detection accuracy without substantially increasing model parameter computations. Experiments on a self-built photovoltaic array infrared defect image dataset show that ST-YOLO, compared to the baseline YOLOv8s, achieves a 15% reduction in model weight, a 2.9% improvement in Precision, and a 1.4% increase in mAP@0.5. Compared to YOLOv7-Tiny and YOLOv5s, ST-YOLO also demonstrates superior detection performance and advantages. This indicates that ST-YOLO has significant application value in photovoltaic defect detection.