SAR图像舰船目标检测的轻量化和特征增强研究

龚峻扬; 付卫红; 方厚章

doi:10.19665/j.issn1001-2400.20230407

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SAR图像舰船目标检测的轻量化和特征增强研究

信息与通信工程 | 更新时间：2024-06-03

- SAR图像舰船目标检测的轻量化和特征增强研究
- Research on lightweight and feature enhancement of SAR image ship targets detection
- 西安电子科技大学学报 2024年51卷第2期页码：96-106
- 作者机构：
  
  1. 西安电子科技大学通信工程学院,陕西西安 710071
  2. 西安电子科技大学计算机科学技术学院,陕西西安 710071
- 作者简介：
  
  [ "龚峻扬(1998—),男,西安电子科技大学硕士研究生,E-mail:[email protected]; " ]
  付卫红(1979—),女,副教授,E-mail:[email protected]
  [ "方厚章(1985—),男,副教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62376204)
- DOI：10.19665/j.issn1001-2400.20230407
  中图分类号： TP391.4
- 纸质出版日期：2024-4-20，
  
  网络出版日期：2023-11-1，
  
  收稿日期：2023-1-14，
扫描看全文
龚峻扬, 付卫红, 方厚章. SAR图像舰船目标检测的轻量化和特征增强研究[J]. 西安电子科技大学学报, 2024,51(2):96-106.

Junyang GONG, Weihong FU, Houzhang FANG. Research on lightweight and feature enhancement of SAR image ship targets detection[J]. Journal of Xidian University, 2024,51(2):96-106.
龚峻扬, 付卫红, 方厚章. SAR图像舰船目标检测的轻量化和特征增强研究[J]. 西安电子科技大学学报, 2024,51(2):96-106. DOI： 10.19665/j.issn1001-2400.20230407.

Junyang GONG, Weihong FU, Houzhang FANG. Research on lightweight and feature enhancement of SAR image ship targets detection[J]. Journal of Xidian University, 2024,51(2):96-106. DOI： 10.19665/j.issn1001-2400.20230407.

摘要

针对合成孔径雷达(SAR)图像中的舰船目标的准确率易受近岸杂波的影响

且现有检测算法复杂度高

在嵌入式设备上的部署难度大的问题

提出一种适用于嵌入式设备的轻量化高精度SAR图像舰船目标检测算法CA-Shuffle-YOLO。基于YOLO v5目标检测算法

对骨干网络进行轻量化及特征精细化提取两个方面的改进

引入轻量化模块以降低网络的计算复杂度

提高推理速度

并引入协同注意力机制模块增强算法对近岸船舶目标的细节信息的提取能力。在特征融合网络中采用加权特征融合以及跨模块融合

增强模型对SAR舰船目标的细节信息的融合能力

同时

利用深度卷积模块降低计算复杂度

提高实时性。通过在SSDD舰船目标检测数据集上的测试及对比实验的结果

表明CA-Shuffle-YOLO的检测准确率约为97.4%

检测帧率为206 FPS

所需运算复杂度为6.1 GFlops

相比原始的YOLO v5

所提方法的检测帧率提升了60 FPS

所需运算复杂度降低为原来的12%。

Abstract

The accuracy of ship targets detection in sythetic aperture radar images is susceptible to the nearshore clutter.The existing detection algorithms are highly complex and difficult to deploy on embedded devices.Due to these problems a lightweight and high-precision SAR image ship target detection algorithm CA-Shuffle-YOLO(Coordinate Shuffle You Only Look Once) is proposed in this article.Based on the YOLO v5 target detection algorithm

the backbone network is improved in two aspects:lightweight and feature refinement.The lightweight module is introduced to reduce the computational complexity of the network and improve the reasoning speed

and a collaborative attention mechanism module is introduced to enhance the algorithm's ability to extract the detailed information on near-shore ship targets.In the feature fusion network

weighted feature fusion and cross-module fusion are used to enhance the ability of the model to fuse the detailed information on SAR ship targets.At the same time

the depth separable convolution is used to reduce the computational complexity and improve the real-time performance.Through the test and comparison experiments on the SSDD ship target detection dataset

the results show that the detection accuracy of CA-Shuffle-YOLO is 97.4%

the detection frame rate is 206FPS

and the required computational complexity is 6.1GFlops.Compare to the original YOLO v5

the FPS of our algorithm is 60FPS higher with the required computational complexity of our algorithm being only the 12% that of the ordinary YOLOv5.

关键词

合成孔径雷达目标检测卷积神经网络特征提取

Keywords

synthetic aperture radarobject detectionconvolutional neural networksfeature extraction

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