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Underwater occluded object recognition with two-stage image reconstruction strategy

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Abstract

The complex underwater environment, such as foreign object occlusion and dim light, causes the feature of underwater objects to be seriously missing. And ripple causes deformation of objects, which greatly increases the difficulty of feature extraction. Existing object recognition models cannot accurately recognize obscured objects due to incomplete features of underwater objects. To solve the above problems, this paper proposes an underwater occlusion object recognition method based on two-stage image reconstruction strategy. Firstly, the salient feature extraction network and the relevant environment feature extraction network are constructed to extract the salient feature and the relevant environment feature respectively. Secondly, the two-stage image reconstruction model with gradient penalty constraints is constructed to obtain finely reconstructed images. Finally, the object recognition with feature adaptive boundary regression is constructed to realize the recognition of finely reconstructed images. To prove the effectiveness of the proposed algorithm, it is compared with the existing object recognition model in datasets with different levels of complexity. The average recognition accuracy of the proposed model is 78.36%, and the recognition rate is improved by 14.16% compared to the original image. Experiments show that the object recognition algorithm proposed in this paper is effective and superior to the existing algorithms.

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Acknowledgements

This work was supported by the Major Science and Technology Project in Henan Province [221100110500], Science and Technology Project of Henan Province [232102320338, 222102210157].

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Authors and Affiliations

  1. School of Mechatronics Electrical Engineering, Henan Institute of Science and Technology, Hongqi, **nxiang, 453003, Henan, China

    Jiyong Zhou

  2. School of Artificial Intelligence, Henan Institute of Science and Technology, Hongqi, **nxiang, 453003, Henan, China

    Tao Xu & Lei Cai

  3. The First Standardization Laboratory, China North Standardization Center, **cheng, Bei**g, 100089, China

    Wantao Guo

  4. School of Information Engineering, Henan Institute of Science and Technology, Hongqi, **nxiang, 453003, Henan, China

    Weishuo Zhao

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  1. Jiyong Zhou
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  2. Tao Xu
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  4. Weishuo Zhao
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  5. Lei Cai
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Correspondence to Tao Xu.

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The authors declare no conflicts of interest regarding the publication of this paper. And The datasets generated and/or analyzed during the present study are available from the corresponding author on reasonable request.

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Jiyong Zhou, Tao Xu, Wantao Guo, Weishuo Zhao and Lei Cai contributed equally to this work.

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Zhou, J., Xu, T., Guo, W. et al. Underwater occluded object recognition with two-stage image reconstruction strategy. Multimed Tools Appl 83, 11127–11146 (2024). https://doi.org/10.1007/s11042-023-15658-6

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