BFNet: A real-time edge-deployable dual-stream boundary-aware network for defect detection of aquatic photovoltaic systems

In the industrial inspection of aquatic photovoltaic (PV) systems, semantic segmentation faces two critical challenges: the extremely low proportion of defect pixels relative to the overall image and the restrictive edge deployment conditions, which necessitate real-time inference on resource-limited devices. This paper introduces BFNet, a boundary-aware architecture employing a dual-stream design to distinctly separate boundary features from semantic representations, thereby addressing the gradient dominance issue caused by majority classes. The main contributions include: (1) a learnable boundary gating mechanism for adaptive edge enhancement; (2) Dilated Dense Blocks that significantly reduce parameter volume while preserving receptive field coverage; and (3) a multi-task training approach weighted by inverse square root frequency. Experimental evaluations demonstrate that BFNet achieves defect detection accuracy statistically comparable to heavyweight methods, yet with substantially fewer parameters, enabling real-time deployment on battery-powered unmanned surface vessels.