Detailed PV Monitor: A Highly Generalized Photovoltaic Panels Segmentation Network Integrating Context-Aware and Deep Feature Reconstruction

Abstract

The urgency of global climate change has driven the rapid expansion of photovoltaic (PV) energy systems. However, accurately identifying PV panels remains a major challenge due to complex environmental backgrounds, spectral confusion, and the lack of high-quality annotated datasets. These factors significantly impact the generalization ability of deep learning models in large-scale high-resolution remote sensing applications, thereby limiting the effective monitoring and planning of PV power stations. To address these challenges, this article proposes a highly adaptable PV panel segmentation network, detailed PV monitoring (DPVM), specifically designed to enhance PV panel recognition in high-resolution imagery. DPVM integrates an adaptive context-aware module (ACAM) and a deep feature reconstruction decoder (DFRD). ACAM improves segmentation accuracy by leveraging multiscale feature fusion and spatial attention mechanisms. DFRD employs multistage decoding and feature synthesis to achieve high-quality image reconstruction. We trained DPVM on our self-constructed Northwest China PV dataset to ensure comprehensive learning of PV panel characteristics. Subsequently, we conducted generalization tests on other publicly available datasets, including AIR-PV and PVP. Experimental results demonstrate that DPVM exhibits outstanding robustness and broad adaptability, ensuring stable performance across diverse scenarios. Specifically, DPVM excels in complex backgrounds, significantly reducing PV panel missed detections, improving edge delineation, and outperforming classical and state-of-the-art segmentation models in key metrics.