Enhancing Rooftop Photovoltaic Segmentation Using Spatial Feature Reconstruction and Multi-Scale Feature Aggregation

Amidst the dual challenges of energy shortages and global warming, photovoltaic (PV) power generation has emerged as a critical technology due to its efficient utilization of solar energy. Rooftops, as underutilized spaces, are ideal locations for installing solar panels, avoiding the need for additional land. However, the accurate and generalized segmentation of large-scale PV panel images remains a technical challenge, primarily due to varying image resolutions, large image scales, and the significant imbalance between foreground and background categories. To address these challenges, this paper proposes a novel model based on the Res2Net architecture, an enhanced version of the classic ResNet optimized for multi-scale feature extraction. The model integrates Spatial Feature Reconstruction and multi-scale feature aggregation modules, enabling effective extraction of multi-scale data features and precise reconstruction of spatial features. These improvements are particularly designed to handle the small proportion of PV panels in images, effectively distinguishing target features from redundant ones and improving recognition accuracy. Comparative experiments conducted on a publicly available rooftop PV dataset demonstrate that the proposed method achieves superior performance compared to mainstream techniques, showcasing its effectiveness in precise PV panel segmentation.