A novel detection method for hot spots of photovoltaic (PV) panels using improved anchors and prediction heads of YOLOv5 network

Accurate classification and detection of hot spots of photovoltaic (PV) panels can help guide operation and maintenance decisions, improve the power generation efficiency of the PV system, and ensure power stations’ safe and stable operation. Considering that, in this paper, the hot spots of PV panels collected on site are taken as the research object, and their formation mechanism is studied. Based on this, the morphological characteristics possessed by the hot spots of PV panels are classified into circular, linear, and array ones. A novel method for detecting hot spots of PV panels based on improved anchors and prediction heads of the YOLOv5 (AP-YOLOv5) network is proposed. Besides, to improve the detection precision of the YOLOv5 network at different scales in hot spots of PV panels, the K-means clustering algorithm is employed to cluster the length–width ratio of the data annotation frame, and a group of the anchors with smaller values is added so as to realize the detection of small targets by optimizing the cluster number. Apart from that, the corresponding prediction heads are constructed for the new anchor parameters to improve the detection precision concerning hot spots of PV panels. Furthermore, the model is verified by training experimental data and comparing test set results. The results showed that compared with other one-stage object detection models, the mean average precision (mAP) of the proposed network can achieve 87.8%, while the average recall rate is 89.0%, and the F1 score reaches 88.9%. In addition, the precision of this model is better than that of other models while maintaining a high frame rate; the frames per second (FPS) is as high as 98.6, thus laying a foundation for developing rapid detection tool of hot spots of PV panels, improving the safety of power station operation, and providing a method for intelligent operation and maintenance of PV power stations.