Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition

The integration of remote sensing and artificial intelligence technologies into photovoltaic (PV) power generation has significantly enhanced the efficiency and precision of monitoring and evaluating PV station construction. However, most semantic segmentation models are primarily developed for natural scenes, often neglecting the distinctive visual attributes of PV panels. We introduce a visual feature constraint method designed to tailor the segmentation network to the unique aspects of PV panels, including their texture, color, and shape. The method incorporates a constraint module, comprised of three adversarial autoencoders, into a conventional segmentation model. This technique represents a versatile training framework that can be seamlessly integrated with state-of-the-art models, providing clear insights into the learning process. Experimental results with UperNet, SegFormer, DeepLabV3+, TransUNet, CorrMatch, SCSM and UKAN as baseline models show a maximum IoU improvement of 2.16 %. Notably, UperNet attains the superior segmentation outcomes, whereas DeepLabV3+ exhibits the greatest benefit from the imposed constraints. Furthermore, our findings reveal that various models exhibit distinct sensitivities to different visual features, and employing multiple constraints typically yields better results than relying on single-feature constraints. Collectively, our proposed method showcases its potential to advance PV panel segmentation in remote sensing applications, presenting a scalable and effective solution.