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Article | Open Access

A Survey of Camouflaged Object Detection and Beyond

Fengyang Xiao1,2, Sujie Hu1, Yuqi Shen1, Chengyu Fang1, Jinfa Huang3, Longxiang Tang1, Ziyun Yang2, Xiu Li1( ), Chunming He1,2( )
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
School of Electrical and Computer Engineering, Peking University, Shenzhen 518055, China

Fengyang Xiao and Sujie Hu contributed equally to this work.

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Abstract

Camouflaged object detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems. In recent years, COD has garnered widespread attention due to its potential applications in surveillance, wildlife conservation, autonomous systems, and more. While several surveys on COD exist, they often have limitations in terms of the number and scope of papers covered, particularly regarding the rapid advancements made in the field since mid-2023. To address this void, we present the most comprehensive review of COD to date, encompassing both theoretical frameworks and practical contributions to the field. This paper explores various COD methods across four domains, including both image-level and video-level solutions, from the perspectives of traditional and deep learning approaches. We thoroughly investigate the correlations between COD and other camouflaged scenario methods, thereby laying the theoretical foundation for subsequent analyses. Furthermore, we delve into novel tasks such as referring-based COD and collaborative COD, which have not been fully addressed in previous works. Beyond object-level detection, we also summarize extended methods for instance-level tasks, including camouflaged instance segmentation, counting, and ranking. Additionally, we provide an overview of commonly used benchmarks and evaluation metrics in COD tasks, conducting a comprehensive evaluation of deep learning-based techniques in both image and video domains, considering both qualitative and quantitative performance. Finally, we discuss the limitations of current COD models and propose 9 promising directions for future research, focusing on addressing inherent challenges and exploring novel, meaningful technologies. This comprehensive examination aims to deepen the understanding of COD models and related methods in camouflaged scenarios. For those interested, a curated list of COD-related techniques, datasets, and additional resources can be found at https://github.com/ChunmingHe/awesome-concealed-object-segmentation.

Keywords

camouflaged object detectioncamouflaged scenario understandingdeep learningartificial intelligence

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CAAI Artificial Intelligence Research
Volume 3,
2024
Article number: 9150044
DOI: 10.26599/AIR.2024.9150044
Cite this article:
Xiao F, Hu S, Shen Y, et al. A Survey of Camouflaged Object Detection and Beyond. CAAI Artificial Intelligence Research, 2024, 3: 9150044. https://doi.org/10.26599/AIR.2024.9150044

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Published: 31 December 2024
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