Incremental structural adaptation for camouflaged object detection

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-05-05 DOI:10.1016/j.imavis.2025.105565

Qingzheng Wang , Jiazhi Xie , Ning Li, Xingqin Wang, Wenhui Liu, Zengwei Mai

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引用次数: 0

Abstract

Camouflaged Object Detection (COD) is a challenging task due to the similarity between camouflaged objects and their backgrounds. Recent approaches predominantly utilize structural cues but often struggle with misinterpretations and noise, particularly for small objects. To address these issues, we propose the Structure-Adaptive Network (SANet), which incrementally supplements structural information from points to surfaces. Our method includes the Key Point Structural Information Prompting Module (KSIP) to enhance point-level structural information, Mixed-Resolution Attention (MRA) to incorporate high-resolution details, and the Structural Adaptation Patch (SAP) to selectively integrate high-resolution patches based on the shape of the camouflaged object. Experimental results on three widely used COD datasets demonstrate that SANet significantly outperforms state-of-the-art methods, achieving more accurate localization and finer edge segmentation, while minimizing background noise. Our code is available at https://github.com/vstar37/SANet/.

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基于增量结构自适应的伪装目标检测

由于伪装目标与其背景的相似性，伪装目标检测是一项具有挑战性的任务。最近的方法主要利用结构线索，但经常与误解和噪音作斗争，特别是对于小对象。为了解决这些问题，我们提出了结构自适应网络（SANet），它从点到面增量地补充结构信息。我们的方法包括关键点结构信息提示模块（KSIP）来增强点级结构信息，混合分辨率注意（MRA）来融合高分辨率细节，结构适应补丁（SAP）来根据伪装对象的形状选择性地集成高分辨率补丁。在三个广泛使用的COD数据集上的实验结果表明，SANet显著优于最先进的方法，实现了更准确的定位和更精细的边缘分割，同时最大限度地减少了背景噪声。我们的代码可在https://github.com/vstar37/SANet/上获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.