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Attention Guided Multi-level Feedback Network for Camouflage Object Detection

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13188))

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Abstract

“Camouflage object detection(COD)” refers to identify objects hidden in the surrounding environment, such as oriental scops owl in a tree hole. Nowadays, due to the high similarity between the camouflaged object and its dependent environment, coupled with the lack of large-scale datasets, the research on this task is still very challenging. Current COD models directly send feature maps output by the backbone into the encoding-decoding module and process them equally, which may cause information interference to a certain extent. In addition, regarding the disappearance of the underlying clues in DCNNs, these models have not been well resolved. This article carries out further research based on the existing models and proposes a novel model, AGMFNet. Specifically, we introduce channel attention and spatial attention to obtain more information we need and suppress useless information to avoid information interference. In order to make feature maps integrate better, the Inception module is utilized. Furthermore, the cascade decoding module is further expanded, and we proposed a multi-level feedback module with auxiliary edge information to refine the camouflage image, which can make full use of the high-level features while retaining the low-level clues. After a series of ablation experiments on the introduced modules on the test datasets, all the combinations can improve the performance, which will also help develop camouflage object detection. The code will be available at: https://github.com/baekqiu/AGMFNet-for-COD/

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Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, 210094, China

    Qiuyan Tang, Jialin Ye, Fukang Chen & Xia Yuan

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  1. Qiuyan Tang
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  2. Jialin Ye
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  3. Fukang Chen
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  4. Xia Yuan
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Corresponding author

Correspondence to Xia Yuan .

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Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Tang, Q., Ye, J., Chen, F., Yuan, X. (2022). Attention Guided Multi-level Feedback Network for Camouflage Object Detection. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_17

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  • DOI: https://doi.org/10.1007/978-3-031-02375-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02374-3

  • Online ISBN: 978-3-031-02375-0

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