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A survey on deep learning-based camouflaged object detection

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Abstract

Camouflaged object detection (COD) is an emerging visual detection task that aims to identify objects that conceal themselves in the surrounding environment. The high intrinsic similarities between the camouflaged objects and their backgrounds make COD far more challenging than traditional object detection. Recently, COD has attracted increasing research interest in the computer vision community, and numerous deep learning-based methods have been proposed, showing great potential. However, most of the existing work focuses on analyzing the structure of COD models, with few overview works summarizing deep learning-based models. To address this gap, we provide a comprehensive analysis and summary of deep learning-based COD models. Specifically, we first classify 48 deep learning-based COD models and analyze their advantages and disadvantages. Second, we introduce widely available datasets for COD and performance evaluation metrics. Then, we evaluate the performance of existing deep learning-based COD models on these four datasets. Finally, we indicate relevant applications and discuss challenges and future research directions for the COD task.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China under Grant(62162013), Guizhou Normal University New Academic Talent Fund Project (Qian Shi Xin Miao [2022] No. 30), and Deep Learning-Based Camouflaged Object Detection–the National Entrepreneurship Training Program(202210663045).

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  1. Department of Big Data and Computer Science, Guizhou Normal University, Guiyang, 550025, China

    Junmin Zhong, Anzhi Wang, Chunhong Ren & Jintao Wu

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  1. Junmin Zhong
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  2. Anzhi Wang
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  3. Chunhong Ren
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Contributions

J.M.: conceptualization, methodology, validation, visualization, data curation, writing-original draft, writing-review. A.Z.: conceptualization, formal analysis, resources, supervision, writing-review. C.H.: formal analysis, data curation, supervision, writing-review. J.T.: data curation, supervision, writing-review. All authors reviewed the manuscript.

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Correspondence to Anzhi Wang.

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Communicated by Junyu Gao.

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Zhong, J., Wang, A., Ren, C. et al. A survey on deep learning-based camouflaged object detection. Multimedia Systems 30, 268 (2024). https://doi.org/10.1007/s00530-024-01478-7

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