伪装目标检测：发展与挑战

doi:10.3778/j.issn.1002-8331.2409-0274

摘要/Abstract

摘要： 伪装目标检测（camouflaged object detection，COD）是计算机视觉领域的一项挑战性任务，致力于识别那些与周围环境高度融合、伪装或隐蔽的目标，主要分为基于手工特征和基于深度学习2种范式。研究人员从单一特征和多特征融合2个角度分析了26种基于手工特征的方法；按发表年份和任务类型梳理了131个在2019年至2024年第二季度期间提出的深度COD模型来揭示其发展现状；基于3种模型调用模式和3类工作方式，分别详细分析了各类深度COD方法的优势与不足；总结了COD的常用数据集、数据增强技术和评价指标，并基于实验对27种前沿的图像级深度COD模型进行了性能评估。基于上述研究探讨了伪装目标检测面临的挑战和未来的发展方向。

关键词: 伪装目标检测, 手工特征, 深度学习, 数据增强

Abstract: Camouflaged object detection (COD) is a challenging task in the field of computer vision, dedicated to identifying targets that are highly integrated with their surroundings, camouflaged, or concealed. It can be distinguished into two paradigms based on manual features and based on deep learning. Firstly, the paper analyzes 26 methods based on manual features from the perspectives of single features and multi-feature fusion. Subsequently, it sorts out 131 deep COD models proposed between 2019 and the second quarter of 2024 by publication year and task type to reveal the current state of development. Furthermore, it provides a detailed analysis of the advantages and disadvantages of various deep COD methods based on three model invocation patterns and three types of operation. At the same time, it summarizes commonly used datasets, data augmentation techniques and evaluation metrics for COD, and conducts performance evaluations on 27 cutting-edge image-level deep COD models based on experiments. Finally, it discusses the challenges existing in COD field and potential future development directions based on the above researches.

Key words: camouflaged object detection, manual feature, deep learning, data augmentation

陈宇, 权冀川. 伪装目标检测：发展与挑战[J]. 计算机工程与应用, 2025, 61(7): 42-60.

CHEN Yu, QUAN Jichuan. Camouflaged Object Detection：Developments and Challenges[J]. Computer Engineering and Applications, 2025, 61(7): 42-60.

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