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Two guidance joint network based on coarse map and edge map for camouflaged object detection

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Abstract

Camouflaged object detection (COD) entails identifying objects in an image that blend with the background. However, most traditional COD methods have not comprehensively considered the information provided by the overall region and edges of the objects. To address this problem, a new two-guidance joint network based on coarse and edge maps is proposed for COD. Particularly, an information guidance module is designed to inject edges and overall information into the network’s backbone features. Meanwhile, a feature observation model based on skip connections and multi-scale perception is designed to capture multi-scale image details and structures. To avoid the loss of semantic information in low-level features, a full-image attention mechanism is designed to integrate high-level features into low-level features, thereby improving the resolution of the object masks. We compared the proposed network with state-of-the-art models on three well-known datasets, and the experimental results show the proposed network has significant improvement. By exploring valuable boundary information and overall object information, the proposed network can segment object edges while also considering the segmentation effect of the entire object. Our code has been open-sourced at https://github.com/Huah2019/TJNet.

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Data Availability

The training datasets can be found in the https://drive.google.com/file/d/1Kifp7I0n9dlWKXXNIbN7kgyokoRY4Yz7/view?usp=sharing. The test datasets can be found in the https://drive.google.com/file/d/1SLRB5Wg1Hdy7CQ74s3mTQ3ChhjFRSFdZ/view?usp=sharing. All the datasets is sourced from Github’s open-source project and can be accessed through https://github.com/thograce/BGNet.

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Author notes

  1. Jing Tang, Dengpeng Zou, Junyi Rao, and Fang Qi contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Zhe Tang, Jing Tang, Dengpeng Zou & Fang Qi

  2. ChangSha XiangFeng Intelligent Equipment Co.,Ltd, Changsha, 410083, Hunan, China

    Zhe Tang

  3. College of Communication Engineering, Jilin University, Changchun, 130012, Jilin, China

    Junyi Rao

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  1. Zhe Tang
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Correspondence to Fang Qi.

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This work is supported by the National Key Research and Development Program (No. 2022YFD2101101), the Project of Scientifc and Technological Innovation Planning of Hunan Province (No.2021NK1020), the earmarked fund for China Agriculture Research System (CARS-19), and the High Performance Computing Center of Central South University.

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Tang, Z., Tang, J., Zou, D. et al. Two guidance joint network based on coarse map and edge map for camouflaged object detection. Appl Intell 54, 7531–7544 (2024). https://doi.org/10.1007/s10489-024-05559-y

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