| 摘 要： |
Inaccurate edge detection is a common challenge in the segmentation of household rooftop photovoltaic (PV) systems from remote sensing images, which hinders the accurate retrieval of PV distribution information critical for planning and managing PV development. A widely adopted solution is to incorporate an additional edge detection task into a joint-task learning framework to enhance edge perception. However, existing joint-task learning methods often struggle to accurately detect PV edges and lack effective mechanisms for distinguishing PV edges from those of similar objects. To address the above challenges, we develop a novel joint-task learning framework. This framework introduces a Scale Adaptive Module (SAM) that dynamically adjusts the receptive field of edge features based on the PV actual size and shape, enabling precise detection of PV edges with varying shapes and sizes. In addition, a Position Guidance Module (PGM) is proposed based on the intrinsic relationship between the PV segmentation task and the edge detection task. The PGM not only guides the edge detection task to focus on identifying the semantic edges of PVs using the distribution information from the segmentation task but also enhances the ability of the segmentation task to accurately locate PVs in complex backgrounds by utilizing the backward gradient from the edge detection task. Multiple rounds of repeated experiments on the Duke and IGN datasets demonstrate the framework's superior performance. Compared to other models, the proposed framework significantly improves the detection accuracy of various PV edges, achieving the best performance in household rooftop PV segmentation with an Intersection over Union (IoU) of 77.4 %. This study provides valuable insights into the accurate acquisition of household rooftop PV information and offers a promising solution for object segmentation tasks facing the challenge of inaccurate edge extraction. |
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