Detection of Photovoltaic Arrays in High-Spatial-Resolution Remote Sensing Images Using a Weight-Adaptive YOLO Model

This study addresses the issue of inadequate remote sensing monitoring accuracy for photovoltaic (PV) arrays in complex geographical environments against the backdrop of rapid global expansion in PV power generation. Particularly concerning the complex spatial distribution characteristics formed by multiple types of PV power stations within China, this study overcomes traditional technical limitations that rely on very high-resolution (0.3–0.8 m) aerial imagery and manual annotation templates. Instead, it proposes an intelligent recognition method for PV arrays based on satellite remote sensing imagery. By enhancing the C3 feature extraction module of the YOLOv5 object detection model and innovatively introducing a weight-adaptive adjustment mechanism, the model’s ability to represent features of PV components across multiple scenarios is significantly improved. Experimental results demonstrate that the improved model achieves enhancements of 6.13% in recall, 3.06% in precision, 5% in F1 score, and 4.6% in mean Average Precision (mAP), respectively. Notably, the false detection rate in low-resolution (<5 m) panchromatic imagery is significantly reduced. Comparative analysis reveals that the optimized model reduces the error rate for small object detection in black-and-white imagery and complex scenarios by 19.8% compared to the baseline model. The technical solution proposed in this study provides a feasible technical pathway for constructing a dynamic monitoring system for large-scale PV facilities.