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WANG Xiang, WANG Yumiao, CHEN Xingyu, et al. Deep learning-based marine target detection method with multiple feature fusion[J]. Journal of Radars, in press. doi: 10.12000/JR23105
Citation: WANG Xiang, WANG Yumiao, CHEN Xingyu, et al. Deep learning-based marine target detection method with multiple feature fusion[J]. Journal of Radars, in press. doi: 10.12000/JR23105
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Deep Learning-based Marine Target Detection Method with Multiple Feature Fusion

doi: 10.12000/JR23105
  • 1.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou 324000, China

Funds:  The National Natural Science Foundation of China (62271126), The Municipal Government of Quzhou (2022D008, 2022D005), The Guangdong Key Areas Research and Development Program (2020B090905002), 111 Project (B17008)
More Information
  • Corresponding author: CUI Guolong, cuiguolong@uestc.edu.cn
  • Received Date: 2023-06-14
  • Rev Recd Date: 2023-07-15
    • Available Online: 2023-07-21
    Abstract
  • Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.

     

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