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<p>Edge detection is a fundamental task in computer vision, pivotal for applications such as image segmentation, reconstruction, and object detection. Despite advancements, existing methods often struggle with accurately preserving fine-grained and low-contrast edges. This paper introduces ERENet, a novel edge refinement and enhancement network leveraging multi-level information fusion. ERENet employs a two-stage architecture featuring a feature extraction module and two plug-and-play modules: an edge enhancement module and an attention fusion module. These components synergistically refine edge lines and emphasize low-level texture details. Evaluated on the BIPED, BSDS500, UDED and NYUDv2 datasets, ERENet demonstrates superior performance across ODS, OIS, and AP metrics, providing clearer boundary features compared to state-of-the-art methods. Our code is available at <a href="https://github.com/shuang2099/ERENet">https://github.com/shuang2099/ERENet</a>.</p>

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ERENet: multi-level information fusion for refined and enhanced edge detection

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Shuang Li,
  • Yicheng Chen,
  • Changqing Li,
  • Chang Feng,
  • Changhai Zhai

摘要

Edge detection is a fundamental task in computer vision, pivotal for applications such as image segmentation, reconstruction, and object detection. Despite advancements, existing methods often struggle with accurately preserving fine-grained and low-contrast edges. This paper introduces ERENet, a novel edge refinement and enhancement network leveraging multi-level information fusion. ERENet employs a two-stage architecture featuring a feature extraction module and two plug-and-play modules: an edge enhancement module and an attention fusion module. These components synergistically refine edge lines and emphasize low-level texture details. Evaluated on the BIPED, BSDS500, UDED and NYUDv2 datasets, ERENet demonstrates superior performance across ODS, OIS, and AP metrics, providing clearer boundary features compared to state-of-the-art methods. Our code is available at https://github.com/shuang2099/ERENet.