Volume 13 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2024  >  13(3): 554-564
WANG Xiang, WANG Yumiao, CHEN Xingyu, et al. Deep learning-based marine target detection method with multiple feature fusion[J]. Journal of Radars, 2024, 13(3): 554–564. 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, 2024, 13(3): 554–564. doi: 10.12000/JR23105
shu

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
  • Publish Date: 2023-08-15
    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.

     

    • FullText(HTML)
  • loading
    • References(28)
  • [1]
    苏宁远, 陈小龙, 关键, 等. 基于深度学习的海上目标一维序列信号目标检测方法[J]. 信号处理, 2020, 36(12): 1987–1997. doi: 10.16798/j.issn.1003-0530.2020.12.004.

    SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. One-dimensional sequence signal detection method for marine target based on deep learning[J]. Journal of Signal Processing, 2020, 36(12): 1987–1997. doi: 10.16798/j.issn.1003-0530.2020.12.004.
    [2]
    赵文静, 刘畅, 刘文龙, 等. K分布海杂波背景下基于最大特征值的雷达信号检测算法[J]. 电子与信息学报, 2018, 40(9): 2235–2241. doi: 10.11999/JEIT171092.

    ZHAO Wenjing, LIU Chang, LIU Wenlong, et al. Maximum eigenvalue based radar signal detection method for K distribution sea clutter environment[J]. Journal of Electronics &Information Technology, 2018, 40(9): 2235–2241. doi: 10.11999/JEIT171092.
    [3]
    陈小龙, 关键, 何友. 微多普勒理论在海面目标检测中的应用及展望[J]. 雷达学报, 2013, 2(1): 123–134. doi: 10.3724/SP.J.1300.2012.20102.

    CHEN Xiaolong, GUAN Jian, and HE You. Applications and prospect of micro-motion theory in the detection of sea surface target[J]. Journal of Radars, 2013, 2(1): 123–134. doi: 10.3724/SP.J.1300.2012.20102.
    [4]
    LO T, LEUNG H, LITVA J, et al. Fractal characterisation of sea-scattered signals and detection of sea-surface targets[J]. IEE Proceedings F (Radar and Signal Processing), 1993, 140(4): 243–250. doi: 10.1049/ip-f-2.1993.0034.
    [5]
    XU Xiaoke. Low observable targets detection by joint fractal properties of sea clutter: An experimental study of IPIX OHGR datasets[J]. IEEE Transactions on Antennas and Propagation, 2010, 58(4): 1425–1429. doi: 10.1109/TAP.2010.2041144.
    [6]
    HU Jing, TUNG W W, and GAO Jianbo. Detection of low observable targets within sea clutter by structure function based multifractal analysis[J]. IEEE Transactions on Antennas and Propagation, 2006, 54(1): 136–143. doi: 10.1109/TAP.2005.861541.
    [7]
    邵夫驰, 行鸿彦. 基于FRFT的多重分形海面小目标检测[J]. 探测与控制学报, 2020, 42(1): 69–74, 80.

    SHAO Fuchi and XING Hongyan. Small target detection based on multi-fractal characteristics of sea clutter FRFT spectrum[J]. Journal of Detection &Control, 2020, 42(1): 69–74, 80.
    [8]
    SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657.
    [9]
    LI Yuzhou, XIE Pengcheng, TANG Zeshen, et al. SVM-based sea-surface small target detection: A false-alarm-rate-controllable approach[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8): 1225–1229. doi: 10.1109/LGRS.2019.2894385.
    [10]
    郭子薰, 水鹏朗, 白晓惠, 等. 海杂波中基于可控虚警K近邻的海面小目标检测[J]. 雷达学报, 2020, 9(4): 654–663. doi: 10.12000/JR20055.

    GUO Zixun, SHUI Penglang, BAI Xiaohui, et al. Sea-surface small target detection based on K-NN with controlled false alarm rate in sea clutter[J]. Journal of Radars, 2020, 9(4): 654–663. doi: 10.12000/JR20055.
    [11]
    YAN Kun, BAI Yu, WU H C, et al. Robust target detection within sea clutter based on graphs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 7093–7103. doi: 10.1109/TGRS.2019.2911451.
    [12]
    时艳玲, 姚婷婷, 郭亚星. 基于图连通密度的海面漂浮小目标检测[J]. 电子与信息学报, 2021, 43(11): 3185–3192. doi: 10.11999/JEIT201028.

    SHI Yanling, YAO Tingting, and GUO Yaxing. Floating small target detection based on graph connected density in sea surface[J]. Journal of Electronics &Information Technology, 2021, 43(11): 3185–3192. doi: 10.11999/JEIT201028.
    [13]
    许述文, 焦银萍, 白晓惠, 等. 基于频域多通道图特征感知的海面小目标检测[J]. 电子与信息学报, 2023, 45(5): 1567–1574. doi: 10.11999/JEIT220188.

    XU Shuwen, JIAO Yinping, BAI Xiaohui, et al. Small target detection based on frequency domain multichannel graph feature perception on sea surface[J]. Journal of Electronics &Information Technology, 2023, 45(5): 1567–1574. doi: 10.11999/JEIT220188.
    [14]
    YAN Yujia, WU Guangxin, DONG Yang, et al. Floating small target detection in sea clutter using mean spectral radius[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4023405. doi: 10.1109/LGRS.2022.3165163.
    [15]
    左磊, 产秀秀, 禄晓飞, 等. 基于空域联合时频分解的海面微弱目标检测方法[J]. 雷达学报, 2019, 8(3): 335–343. doi: 10.12000/JR19035.

    ZUO Lei, CHAN Xiuxiu, LU Xiaofei, et al. A weak target detection method in sea clutter based on joint space-time-frequency decomposition[J]. Journal of Radars, 2019, 8(3): 335–343. doi: 10.12000/JR19035.
    [16]
    陈世超, 高鹤婷, 罗丰. 基于极化联合特征的海面目标检测方法[J]. 雷达学报, 2020, 9(4): 664–673. doi: 10.12000/JR20072.

    CHEN Shichao, GAO Heting, and LUO Feng. Target detection in sea clutter based on combined characteristics of polarization[J]. Journal of Radars, 2020, 9(4): 664–673. doi: 10.12000/JR20072.
    [17]
    XU Shuwen, ZHENG Jibin, PU Jia, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1505–1509. doi: 10.1109/LGRS.2018.2852560.
    [18]
    施赛楠, 杨静, 董泽远. 基于高维特征域随机森林的海面小目标检测[J]. 现代雷达, 2022, 44(3): 63–69. doi: 10.16592/j.cnki.1004-7859.2022.03.011.

    SHI Sainan, YANG Jing, and DONG Zeyuan. Detection of small sea-surface target based on random forest in high-dimensional feature domain[J]. Modern Radar, 2022, 44(3): 63–69. doi: 10.16592/j.cnki.1004-7859.2022.03.011.
    [19]
    CHEN Simin, FENG Chen, HUANG Yong, et al. Small target detection in X-band sea clutter using the visibility graph[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5115011. doi: 10.1109/TGRS.2022.3186283.
    [20]
    WANG Jin’gang and LI Songbin. Maritime radar target detection in sea clutter based on CNN with dual-perspective attention[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3500405. doi: 10.1109/LGRS.2022.3230443.
    [21]
    WAN Hao, TIAN Xiaoqing, LIANG Jing, et al. Sequence-feature detection of small targets in sea clutter based on Bi-LSTM[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4208811. doi: 10.1109/TGRS.2022.3198124.
    [22]
    WANG Yumiao, ZHAO Wenjing, WANG Xiang, et al. Nonhomogeneous sea clutter suppression using complex-valued U-Net model[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4027705. doi: 10.1109/LGRS.2022.3214633.
    [23]
    QU Qizhe, WANG Yongliang, LIU Weijian, et al. A false alarm controllable detection method based on CNN for sea-surface small targets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4025705. doi: 10.1109/LGRS.2022.3190865.
    [24]
    李骁, 施赛楠, 董泽远, 等. 基于时频域深度网络的海面小目标特征检测[J]. 雷达科学与技术, 2022, 20(2): 209–216, 230. doi: 10.3969/j.issn.1672-2337.2022.02.013.

    LI Xiao, SHI Sainan, DONG Zeyuan, et al. Feature detection of small sea-surface target via deep network in time-frequency domain[J]. Radar Science and Technology, 2022, 20(2): 209–216, 230. doi: 10.3969/j.issn.1672-2337.2022.02.013.
    [25]
    CHEN Xiaolong, SU Ningyuan, HUANG Yong, et al. False-alarm-controllable radar detection for marine target based on multi features fusion via CNNs[J]. IEEE Sensors Journal, 2021, 21(7): 9099–9111. doi: 10.1109/JSEN.2021.3054744.
    [26]
    IPIX Radar. The McMaster IPIX radar sea clutter database[EB/OL]. http://soma.ece.mcmaster.ca/ipix/, 2021.
    [27]
    TRABELSI C, BILANIUK O, ZHANG Ying, et al. Deep complex networks[C]. 6th International Conference on Learning Representations, Vancouver, Canada, 2018: 1–19.
    [28]
    刘宁波, 丁昊, 黄勇, 等. X波段雷达对海探测试验与数据获取年度进展[J]. 雷达学报, 2021, 10(1): 173–182. doi: 10.12000/JR21011.

    LIU Ningbo, DING Hao, HUANG Yong, et al. Annual progress of the sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2021, 10(1): 173–182. doi: 10.12000/JR21011.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2963) PDF downloads(1086) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return