Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年4月20日

查看全文
  • Mingyu Zhang,
  • Jiacheng Cui,
  • Zizhen Zhao,
  • Erchao Fang,
  • Xiaolei Liu,
  • Shinan Zhao,
  • Jianlin Gao,
  • Yujiang Hong,
  • Mingxi Li

摘要

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.