The Research on Lightweight SAR Ship Detection Method based on Regression Model and Attention
Author:
Affiliation:

1.Key Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;2.University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, P.R.China;3.Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, No.1, Sub-Lane Xiangshan, Xihu District, 310024, Hangzhou,China

Clc Number:

Fund Project:

Supported by National Natural Science Foundation of China(61975222)and CASEarth Minisatellite Thermal Infrared Spectrometer Project(XDA19010102)

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Synthetic aperture radar (SAR) has the advantages of all-sky and all-weather earth observation without cloud interference. Ship detection based on SAR images has been widely used in civil and military fields, including maritime search and rescue, port reconnaissance, territorial sea defense. However, different from large ships, the misdetection rate of small ships with fewer pixels and lower contrast is high. And it is difficult to balance speed and accuracy during on-orbit ship detection. To solve the above problems, an improved lightweight ships detection method (ImShips) based on YOLOv5s is proposed. Firstly, the standard convolution with small receptive field is adopted at the bottom of the baseline to obtain spatial information of small ships. And the dilated convolution with enlarged receptive field is added at the top of the baseline to preserve more semantic features, which is conducive to extract large targets feature. Then, a lightweight channel attention mechanism is applied to the backbone and neck of YOLOv5. And the weight is allocated to filter more important texture information. Finally, the depth-wise separable convolution is adopted to replace the standard convolution during down-sampling to reduce the number of parameters and improve the inference speed. Compared with YOLOv5s model, the experimental results show that ImShips achieves an increase in AP, while the FLOPs are reduced by 45.61%, and the speed is increased by 8.31% in SSDD and ISSID datasets. The speed and accuracy of ImShips model are improved effectively for sea surface object detection. The proposed method has great application potential in on-orbit ship detection.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:September 26,2021
  • Revised:October 20,2021
  • Adopted:October 20,2021
  • Online: October 20,2021
  • Published: