Lightweight attention-based SAR ship detector

YU Nanjing; FENG Daquan; ZHU Ying; ZHANG Hengjia; LU Ping

doi:10.11959/j.issn.2096-3750.2024.00407

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Lightweight attention-based SAR ship detector

Theory and Technology | 更新时间：2025-01-20

- Lightweight attention-based SAR ship detector
- Chinese Journal on Internet of Things Vol. 8, Issue 4, Pages: 156-166(2024)
- 作者机构：
  
  1.深圳大学电子与信息工程学院，广东深圳 518060
  2.中国信息通信研究院，北京 100191
  3.中兴通讯股份有限公司，广东深圳 518055
  4.移动网络和移动多媒体技术国家重点实验室，广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00407
  CLC：
- Received：03 June 2024，
  
  Revised：2024-09-27，
  
  Published：10 December 2024
- 稿件说明：
移动端阅览
于楠晶,冯大权,朱颖等.基于注意力机制的轻量级SAR船舶检测器[J].物联网学报,2024,08(04):156-166.

YU Nanjing,FENG Daquan,ZHU Ying,et al.Lightweight attention-based SAR ship detector[J].Chinese Journal on Internet of Things,2024,08(04):156-166.
于楠晶,冯大权,朱颖等.基于注意力机制的轻量级SAR船舶检测器[J].物联网学报,2024,08(04):156-166. DOI： 10.11959/j.issn.2096-3750.2024.00407.

YU Nanjing,FENG Daquan,ZHU Ying,et al.Lightweight attention-based SAR ship detector[J].Chinese Journal on Internet of Things,2024,08(04):156-166. DOI： 10.11959/j.issn.2096-3750.2024.00407.

摘要

合成孔径雷达（SAR

synthetic aperture radar）遥感图像凭其全天候、全时段优势，在军事侦察、交通监管等领域得到了广泛的应用。卷积神经网络因其较强的学习能力，被广泛用于SAR图像船舶检测算法。然而，SAR图像中船舶特征提取难度较大。此外，计算资源和内存空间受限，实际应用对算法推理速度需求较高。为此，提出了一种基于注意力的轻量级船舶检测（LASD

lightweight attention-based ship detector）算法。该算法设计了一种新的线性混合注意力残差模块，先后用全局通道注意力和局部空间注意力在深层特征空间中提取船舶潜在特征；基于跨阶段部分通道连接的空间金字塔池化模块优化多尺度特征融合质量，用串联小核池化组替换并行大核池化组以提升算法推理速度；设计了一种新的基于局部注意力的特征融合策略，在特征融合阶段利用局部注意力进一步扩大船舶和背景噪声的差异。在公开数据集SSDD和LS-SSDD-v1.0上的实验数据表明，LASD算法同时兼顾了检测精度和推理速度，相比其他先进算法更具竞争力。

Abstract

Synthetic aperture radar (SAR) remote sensing images have been widely applied in military reconnaissance and traffic supervision

owing to their all-weather and all-day abilities. With excellent learning performance

convolutional neural networks are employed in the SAR ship detection algorithms. However

it is difficult to extract features. In practical applications

computing resources and memory space are limited

and high inference speed is required. Therefore

a lightweight attention-based ship detector (LASD) was proposed. A novel linear hybrid attention module was designed which extracted potential ship features from deep-level space by using global channel attention and local spatial attention. A spatial pyramid pooling module based on cross-stage partial connections optimized the quality of multi-scale feature fusion

which replaced the parallel max-pooling group with large kernels with the serial max-poolings with small kernels to improve the inference speed. A novel feature fusion scheme via the local channel attention was suggested which widened the gap between the objects and background noise using local attention during the feature fusion. The results on the public datasets SSDD and LS-SSDD-v1.0 show that LASD achieves the balance of detection precision and inference speed

and is more competitive than the other advanced algorithms.

关键词

Keywords

references

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