CEMP-YOLO: An infrared overheat detection model for photovoltaic panels in UAVs

Abstract

Aiming at the complex working conditions of actual PV power stations, traditional PV panel detection methods employed by operators still result in some faults and safety risks. Under the framework of the YOLOv10n model, a CEMP-YOLOv10n-based infrared image detection algorithm for photovoltaic power plants is proposed. The improvements in CEMP-YOLOv10n comprise four main components. The ABCG_Block structure was designed, and the C2f structure within the Backbone component was optimized to enhance feature extraction capabilities. The ERepGFPN structure is used in the Neck component to retain semantic information and fuse features between high and low layers. The detector head was optimized with PConv convolution to minimize redundant information. Finally, MECA attention was added before P3, P4, and P5 detection heads to enhance adaptive recognition and accuracy.Experimental validation using infrared UAV imagery of PV panels shows the model's computational cost decreased to 4.7 GFLOPs, 72.3 % of the original. Parameters and weights decreased by 25.99 % and 24.13 %, respectively, while accuracy and mean average precision (mAP) improved by 8.3% and 2 %, reaching 86.6 % and 87.3 %. Compared to 13 YOLO-series algorithms, including DETR, YOLOv8n, YOLOv9-tiny, and YOLOv11n, the CEMP-YOLOv10n model demonstrates superior accuracy, parameter efficiency, and memory consumption. The CEMP-YOLOv10n model significantly improves defect recognition accuracy, reduces missed detections, and balances lightweight design with detection speed. This lays the foundation for future UAV inspection edge device deployment and smart PV big data platform creation.