Model validation results.

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.光伏面板是光伏发电系统的核心组件，其质量直接影响发电效率与电路运行安全。针对现有光伏缺陷检测技术存在的人工成本高昂、检测工作量庞大、传感器故障率偏高、可靠性不足、误报率高、网络需求严苛以及传统算法检测速度缓慢等诸多弊端，本文提出一种专用于光伏组件缺陷检测的算法ST-YOLO，该算法基于YOLOv8s构建。首先，该算法引入基于SCconv卷积的C2f-SCconv卷积模块，该模块通过轻量化设计降低模型参数的计算负载，提升检测速度；此外，融入三元注意力（Triplet Attention）机制，在未显著增加模型参数计算量的前提下，大幅提升检测精度。在自建光伏阵列红外缺陷图像数据集上开展的实验结果显示：相较于基线模型YOLOv8s，ST-YOLO的模型权重降低15%，精确率（Precision）提升2.9%，mAP@0.5提升1.4%；相较于YOLOv7-Tiny与YOLOv5s，ST-YOLO同样展现出更优异的检测性能与综合优势。这表明ST-YOLO在光伏缺陷检测领域具备显著的应用价值。