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Underwater target detection with an attention mechanism and improved scale

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A Correction to this article was published on 15 October 2022

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Abstract

The light problem and the complicated environment of underwater images make target detection difficult. These images are usually blurry because tiny inorganic and organic particles in the water have a great impact on light. To solve this problem, we add squeeze and excitation modules after the deep convolution layers of the YOLOv3 model to learn the relationship between channels and enhance the semantic information of deep features. In addition, many small targets will lose too much information after five downsamples. This is not conducive to detection. By expanding the detection scale, we combine the deep semantic information with the location information of the shallower layer to improve the detection performance of small targets. The experimental results show that the YOLOv3-brackish model greatly improved the detection of small fish, crabs, shrimp and starfish. In addition, there were minor improvements in the detection of big fish and jellyfish. The mean average precision increased by 4.43%.

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Acknowledgements

We thank the anonymous reviewers for their insightful comments. This work was supported by the Key Program of National Natural Science Foundation of China (U2003208), Major science and technology projects in the autonomous region (2020A03004-4) and the real-time underwater specific target autonomous recognition project (2019750001).

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, 830000, China

    Xiangyu Wei & Pengcheng Feng

  2. College of Network Center, Xinjiang University, Urumqi, 830000, China

    Long Yu

  3. Signal and Signal Processing Laboratory, College of Information Science and Engineering, Xinjiang University, Urumqi, 830000, China

    Long Yu

  4. College of Software, Xinjiang University, Urumqi, 830000, China

    Shengwei Tian

  5. Key Laboratory of Software Engineering Technology, College of Software, Xin Jiang University, Urumuqi, 830000, China

    Shengwei Tian

  6. Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100000, China

    Xin Ning

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  1. Xiangyu Wei
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  2. Long Yu
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  3. Shengwei Tian
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  4. Pengcheng Feng
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  5. Xin Ning
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Contributions

XW contributed to the conception of the study, experiments and papers. LY and ST contributed to the guidance of experiments and papers. PF and XN participated in the experiments and papers and proposed constructive suggestions.

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Correspondence to Long Yu.

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The original online version of this article was revised: The Fig. 9 caption in the original publication contains a mistake and the reference referred to is incomplete.

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Wei, X., Yu, L., Tian, S. et al. Underwater target detection with an attention mechanism and improved scale. Multimed Tools Appl 80, 33747–33761 (2021). https://doi.org/10.1007/s11042-021-11230-2

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