DENSE DOCKED SHIP DETECTION VIA SPATIAL GROUP-WISE ENHANCE ATTENTION IN SAR IMAGES

被引:15
|
作者
Wang, Xiaoya [1 ]
Cui, Zongyong [1 ]
Cao, Zongjie [1 ]
Dang, Sihang [1 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu 611731, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Ship detection; SAR iamges; Anchor-free; Convolutional neural network; Dense docking;
D O I
10.1109/IGARSS39084.2020.9324162
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Target detection for dense docked SAR ships has always been a challenge. First of all, dense docked ships are generally in the port area, and the interference in the land area is large. Secondly, the adjacent ships are easily detected as a ship in the detection, or will be suppressed during the Non Maximal Suppression (NMS) process, causing the targets to be lost. This paper proposes a target detection method for dense docked ships based on CenterNet. At the same time, Spatial Group-wise Enhance (SGE) attention module is added to CenterNet in this paper. SGE reduces the amount of calculation by grouping channels, and at the same time strengthens the spatial features of each group to extract more semantic features. The enhanced feature map is sent to the detection network for dense docked SAR ship target detection. The proposed method is verified on the dataset SAR-ship-Dataset, and the experimental results show that the method in this paper has better detection performance for dense docked ships.
引用
收藏
页码:1244 / 1247
页数:4
相关论文
共 50 条
  • [21] Ship detection in SAR images based on super dense feature pyramid networks
    Han Z.
    Wang C.
    Fu Q.
    Xu Y.
    Wang, Chunping (370119128@126.com), 1600, Chinese Institute of Electronics (42): : 2214 - 2222
  • [22] Learning group-wise spatial attention and label dependencies for multi-task thoracic disease classification
    Xu, Yujia
    Lam, Hak-Keung
    Bao, Xinqi
    Wang, Yuhan
    NEUROCOMPUTING, 2024, 573
  • [23] Multitask fMRI Data Classification via Group-wise Hybrid Temporal and Spatial Sparse Representations
    Song, Limei
    Ren, Yudan
    Hou, Yuqing
    He, Xiaowei
    Liu, Huan
    ENEURO, 2022, 9 (03)
  • [24] SAR Ship Detection Algorithm Based on Deep Dense Sim Attention Mechanism Network
    Shan, Huilin
    Fu, Xiangwei
    Lv, Zongkui
    Zhang, Yinsheng
    IEEE SENSORS JOURNAL, 2023, 23 (14) : 16032 - 16041
  • [25] A Decoupled Head and Coordinate Attention Detection Method for Ship Targets in SAR Images
    Li, Qinzuo
    Xiao, Dengjun
    Shi, Fangying
    IEEE ACCESS, 2022, 10 : 128562 - 128578
  • [26] An Enhanced Shuffle Attention with Context Decoupling Head with Wise IoU Loss for SAR Ship Detection
    Tang, Yunshan
    Zhang, Yue
    Xiao, Jiarong
    Cao, Yue
    Yu, Zhongjun
    Remote Sensing, 2024, 16 (22)
  • [27] Ship Detection in Polarimetric SAR Images via Variational Bayesian Inference
    Song, Shengli
    Xu, Bin
    Yang, Jian
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2017, 10 (06) : 2819 - 2829
  • [28] Ship Detection in SAR Images via Local Contrast of Fisher Vectors
    Wang, Xueqian
    Li, Gang
    Zhang, Xiao-Ping
    He, You
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (09): : 6467 - 6479
  • [29] Inshore Dense Ship Detection in SAR Images Based on Edge Semantic Decoupling and Transformer
    Zhou, Yongsheng
    Zhang, Feixiang
    Yin, Qiang
    Ma, Fei
    Zhang, Fan
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 4882 - 4890
  • [30] Detection of Surface Defects in Solar Cells by Bidirectional-Path Feature Pyramid Group-Wise Attention Detector
    Chen, Haiyong
    Song, Mengyuan
    Zhang, Zezhi
    Liu, Kun
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71