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<p>Camouflaged object detection (COD) aims to segment objects that are visually indistinguishable from their surroundings. The intrinsic low boundary contrast and ambiguity of camouflaged instances pose critical challenges to accurate segmentation. To address the subtle boundaries and ambiguous semantics of camouflaged instances, we propose an edge-guided remapping fusion network (ERNet), a prior-optimized framework that integrates edge cues into the segmentation process, progressively reconstructing object boundaries and refining predictions. Specifically, we introduce an edge detection module (EDM) that employs multi-directional depth-wise convolutions to extract structure-aware edge information. To further enhance feature representation, we design a context-boundary-aware feature aggregation (CFA) module that incorporates spatially refined edge priors into multi-scale contextual integration. Moreover, we propose a learnable edge–mask optimization strategy, which leverages the discrepancy between predicted masks and edge maps as a guidance signal for value space remapping, enabling more accurate recovery of missing regions near object boundaries. Experiments conducted on four benchmark datasets demonstrate that our ERNet achieves state-of-the-art performance while producing more precise and complete object boundaries. Our code is publicly available at: <a href="https://github.com/AaHa123/ERNet.">https://github.com/AaHa123/ERNet.</a></p>

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ERNet: a prior-optimized framework for camouflaged object detection

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Mengjiao Lu,
  • Wanjun Wang,
  • Mingyong Pang

摘要

Camouflaged object detection (COD) aims to segment objects that are visually indistinguishable from their surroundings. The intrinsic low boundary contrast and ambiguity of camouflaged instances pose critical challenges to accurate segmentation. To address the subtle boundaries and ambiguous semantics of camouflaged instances, we propose an edge-guided remapping fusion network (ERNet), a prior-optimized framework that integrates edge cues into the segmentation process, progressively reconstructing object boundaries and refining predictions. Specifically, we introduce an edge detection module (EDM) that employs multi-directional depth-wise convolutions to extract structure-aware edge information. To further enhance feature representation, we design a context-boundary-aware feature aggregation (CFA) module that incorporates spatially refined edge priors into multi-scale contextual integration. Moreover, we propose a learnable edge–mask optimization strategy, which leverages the discrepancy between predicted masks and edge maps as a guidance signal for value space remapping, enabling more accurate recovery of missing regions near object boundaries. Experiments conducted on four benchmark datasets demonstrate that our ERNet achieves state-of-the-art performance while producing more precise and complete object boundaries. Our code is publicly available at: https://github.com/AaHa123/ERNet.