Infrared dim-small target detection under complex background based on attention mechanism

LIU Ying; SUN Hai-jiang; ZHAO Yong-xian

doi:10.37188/CJLCD.2023-0030

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Infrared dim-small target detection under complex background based on attention mechanism

Object Detection and Recognition | 更新时间：2023-11-03

- Infrared dim-small target detection under complex background based on attention mechanism
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 11, Pages: 1455-1467(2023)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
  2.中国科学院大学，北京 100049
- 作者简介：
- 基金信息：
  
  Jilin Province Science and Technology Development Plan(20200404155YY;20200401091GX)
- DOI：10.37188/CJLCD.2023-0030
  CLC：
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LIU Ying, SUN Hai-jiang, ZHAO Yong-xian. Infrared dim-small target detection under complex background based on attention mechanism. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1455-1467(2023)
DOI：

LIU Ying, SUN Hai-jiang, ZHAO Yong-xian. Infrared dim-small target detection under complex background based on attention mechanism. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1455-1467(2023) DOI： 10.37188/CJLCD.2023-0030.

摘要

针对复杂场景下红外图像中弱小目标像素占比少、特征细节不明显致使目标特征提取困难、检测准确率低的问题，提出了一种基于注意力机制的复杂背景下红外弱小目标检测方法。该方法以YOLOv5网络为基础，设计SimAMC3注意力机制模块，优化网络的特征提取层；设计目标检测头，通过增加特征融合层来改变其开始进行特征提取的深度，获得新的弱小目标检测层，使浅层特征层更好地保留弱小目标的空间信息；改进预测框筛选方式，提高距离相近或重叠目标的检测精度。实验选取了两个SIRST红外弱小目标图像数据集，对其进行标注并训练。实验结果表明，改进后的算法与原YOLOv5算法相比，平均精度均值（mAP）分别提升了4.8%和7.1%，在不同复杂背景下均可有效检测出红外弱小目标，体现了良好的鲁棒性和适应性，可以有效应用于复杂背景中的红外弱小目标检测。

Abstract

The small target in infrared images has fewer pixels and lack of obvious feature details in complex scenes， which make it difficult to extract target features and usually has low detection accuracy. This paper proposes an infrared small target detection method based on attention mechanism under complex background. Based on YOLOv5 （You Only Look Once） network， SimAMC3 attention mechanism module is designed to optimize the feature extraction layer of the network. The target detection head is designed by adding a feature fusion layer to change the depth of feature extraction， a new weak target detection layer can be obtained， so that the shallow feature layer can better retain the spatial information of the weak target. Finally， the screening method of prediction box is improved to increase the detection accuracy of objects with close distance or overlapping. In the experiment， two SIRST infrared dim-small target image datasets are selected to label and train them. The experimental results show that compared with the original YOLOv5 algorithm， the improved algorithm improves the mean average accuracy （mAP） by 4.8% and 7.1%. It can effectively detect infrared dim-small targets under different complex backgrounds， reflecting good robustness and adaptability， so it can be effectively applied to detect infrared dim-small target.

关键词

深度学习红外弱小目标目标检测注意力机制

Keywords

deep learninginfrared dim-small targettarget detectionattention mechanism

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