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HE Mengyun, HE Zifen, ZHANG Yinhui, CHEN Guangchen, . Weak feature confocal channel regulation for underwater sonar target detection[J]. Chinese Optics. doi: 10.37188/CO.2024-0031
Citation: HE Mengyun, HE Zifen, ZHANG Yinhui, CHEN Guangchen, . Weak feature confocal channel regulation for underwater sonar target detection[J]. Chinese Optics. doi: 10.37188/CO.2024-0031

Weak feature confocal channel regulation for underwater sonar target detection

doi: 10.37188/CO.2024-0031
Funds:  Supported by the National Natural Science Foundation of China ( No. 62171206,No.62061022)
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  • Corresponding author: zyhhzf1998@163.com
  • Received Date: 05 Feb 2024
  • Accepted Date: 26 Apr 2024
  • Available Online: 17 May 2024
  • Visual detection of sonar image is one of the important technologies in the field of resource exploration in complex waters and underwater foreign object target detection. Aiming at the problem of weak features and background information interference of small targets in sonar images, this paper proposes a weak feature confocal channel modulation algorithm for underwater sonar target detection. Firstly, in order to improve the model's ability to capture and characterize the information of weak targets, we design a weak target-specific activation strategy and introduce an a priori frame scale calibration mechanism to match the underlying semantic feature detection branch to improve the accuracy of small target detection; secondly, we apply the global information aggregation module to deeply excavate the global features of weak targets to avoid the redundant information from covering the small target's weak key features; lastly, in order to solve the problem of the traditional space pyramid Finally, in order to solve the problem of traditional spatial pyramid pooling which is easy to ignore the channel information, the confocal channel regulation pooling module is proposed to retain the effective channel domain small target information and overcome the interference of complex background information. Experiments show that the model in this paper achieves an average detection accuracy of 83.3% on nine types of weak targets in the underwater sonar dataset, which is 5.5% higher than the benchmark, among which the detection accuracy of iron bucket, human body model and cube is significantly improved by 24%, 8.6% and 7.3%, respectively, which effectively improves the problem of leakage and misdetection of weak targets in the underwater complex environment.


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