HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections

Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT - YOLO, a lightweight artificial intelligence (AI) model that integrat es an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone - based thermal inspections of PV systems, addressing the unique challenges of detecting small and subtl e thermal anomalies, such as hotspots and defective modules, while maintaining real - time performance. Experimental results demonstrate a mean a verage p recision of 90.8%, reflecting a significant improvement over baseline object detection models. With a reduced computational load and robustness under diverse environmental conditions, HOTSPOT - YOLO offers a scalable and reliable solution for large - scale PV inspections. This work highlights the integration of advanced AI techniques with practical engineering ap plications, revolutionizing automated fault detection in renewable energy systems.