Study on defect detection and identification of solar photovoltaic (PV) panels based on improved YOLO11.

Aiming at the problems of current solar photovoltaic (PV) panel defect detection methods, this paper proposes a solar PV panel defect detection and identification algorithm based on improved YOLO11. The algorithm introduces three key enhancements: (1) a Deformable Attention module that strengthens the model’s ability to extract and learn defect features, effectively reducing false detections in complex backgrounds. (2) a Multi-scale Screening-feature Fusion Pyramid Network that improves adaptability to tiny defects under multi-scale conditions, significantly enhancing detection precision. (3) Structural Reparameterization that reduces model parameters and computational complexity, facilitating deployment on resource-constrained devices. The experimental results on the PVEL-AD-2021 dataset show that the precision, recall, and mAP@0.5 improved by 1.6%, 3%, and 3.8%, respectively, compared to the classical YOLO11. Meanwhile, the number of parameters and computation complexity of the model are reduced by 32.1%, and 22.2%, respectively. The algorithm provides a reliable and practical method for detecting defects in outdoor solar PV panels, which can effectively reduce energy loss and operation and maintenance costs caused by panel defects, while simultaneously enhancing the safety of solar PV systems.