An effective approach to improving photovoltaic defect detection using the new DCD-YOLOv8s model

Photovoltaic (PV) systems play a vital role in the global transition to renewable energy, yet their efficiency is often compromised by surface defects such as dust accumulation, bird droppings, and cracks. Traditional inspection methods are inefficient, while existing deep learning-based detection models struggle with limited adaptability, large model sizes, and inadequate performance under real-world conditions. To address these challenges, we propose the DCD-YOLOv8s algorithm-an enhanced version of the YOLOv8 architecture that integrates deformable convolutional networks (DCNv3), coordinate attention (CA), and dynamic head (DyHead) modules. These enhancements are designed to strengthen feature extraction, object localization, and detection accuracy while minimizing computational overhead. A custom dataset was constructed by combining a public PV panel defect database with field-collected images, further expanded through data augmentation and self-training strategy. Experimental results demonstrated that DCD-YOLOv8s achieved superior results, with an F1-score of 92.8%, mAP@50 of 95.0%, and mAP@50-95 of 82.3%, while maintaining a high inference speed of 45.9 FPS. Comparative evaluations against YOLOv5s, YOLOv6s, YOLOv7s, YOLOv8s, YOLOv10s, RT-DETR-R18, and YOLOv11s confirm its superior performance of DCD-YOLOv8s in identifying PV surface defects. Ablation studies validated the individual and combined efficacy of the integrated modules. Although real-time UAV-based deployment was not conducted, a mission planning framework was proposed. These results highlight DCD-YOLOv8s's strong potential for integration into real-time UAV-based inspection systems, contributing to cost-effective and reliable PV system maintenance.