PHSNet: A Small-Target Infrared Hotspot Detection Network for Photovoltaic Modules in UAV Remote-Sensing Images

With the rapid expansion of global photovoltaic (PV) installed capacity, hot spot defects have become a major hidden danger that reduces power generation efficiency and threatens the safe and stable operation of PV stations. Unmanned aerial vehicle (UAV) infrared remote sensing is a key technology for the efficient intelligent monitoring of large-scale PV stations. However, detecting tiny hotspots in such infrared images poses severe challenges. Most of these defects are ultra-small targets with extremely low pixel size and weak contrast, which are easily submerged by complex background noise, leading to prominent issues including low detection accuracy and high miss rates. To address these issues, we propose a lightweight detection network based on YOLO11n, named PHSNet, for PV hotspot detection in UAV infrared images. Its core designs include the dynamic convolution integrated C3k2 (Dy-C3k2) for small target feature enhancement, context-guided downsampling (CG-Down) to alleviate feature loss during downsampling, optimized detection layers, and a lightweight shared deconvolutional detection head (LSDECD) for small target adaptation in low-altitude aerial scenes, forming a full-link optimization architecture for tiny target feature perception. Experiments on a dedicated dataset (4025 images, 25,181 annotations, 92% targets < 20 pixels) show that PHSNet achieves 0.73 AP50 and 0.315 AP, surpassing YOLO11n by 0.1 in AP50 and 0.058 in AP, respectively. With only 1.8 M parameters and 98.8 FPS, it outperforms mainstream lightweight models, including YOLOv8n and RT-DETR-R18, strikes a superior accuracy–efficiency balance, and provides an efficient solution for real-time intelligent monitoring and edge deployment of PV stations.