Multi-label infrared image classification algorithm based on weakly supervised learning

MIAO Chuankai; LOU Shuli; LI Ting; CAI Huimin

doi:10.37188/OPE.20223020.2501

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Multi-label infrared image classification algorithm based on weakly supervised learning

Information Sciences | 更新时间：2022-10-27

- Multi-label infrared image classification algorithm based on weakly supervised learning
- Optics and Precision Engineering Vol. 30, Issue 20, Pages: 2501-2509(2022)
- 作者机构：
  
  1.烟台大学物理与电子信息学院，山东烟台 264005
  2.天津津航技术物理研究所，天津 300308
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223020.2501
  CLC： TN219;TP391.4
- Received：09 May 2022，
  
  Revised：17 June 2022，
  
  Published：25 October 2022
- 稿件说明：
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苗传开,娄树理,李婷等.基于弱监督学习的多标签红外图像分类算法[J].光学精密工程,2022,30(20):2501-2509.

MIAO Chuankai,LOU Shuli,LI Ting,et al.Multi-label infrared image classification algorithm based on weakly supervised learning[J].Optics and Precision Engineering,2022,30(20):2501-2509.
苗传开,娄树理,李婷等.基于弱监督学习的多标签红外图像分类算法[J].光学精密工程,2022,30(20):2501-2509. DOI： 10.37188/OPE.20223020.2501.

MIAO Chuankai,LOU Shuli,LI Ting,et al.Multi-label infrared image classification algorithm based on weakly supervised learning[J].Optics and Precision Engineering,2022,30(20):2501-2509. DOI： 10.37188/OPE.20223020.2501.

摘要

红外图像的场景感知与分类分级是图像识别的一项关键技术，对于红外侦察与制导具有重要意义。为有效解决红外图像多场景多目标的场景感知及分类分级的问题，本文提出一种基于弱监督学习的多标签红外图像分类算法。将多标签图像分类技术应用于红外前视图像领域，针对多场景的红外图像进行弱监督的图像级标注，使用主干网络Resnet-50对图像进行特征提取；引入类特定的空间残差注意力模块CSRA以捕捉图像中不同类别所占据的不同空间区域，提高类别特征的表达性能；引入先进的损失函数ASL以解决多标签分类中正负标签数量失衡问题，使训练过程中更多地关注阳性样本，提高检测准确率。试验结果表明，本文算法对于多场景多目标的红外图像分类具有更好的适应性和准确率，算法检测率可达90%以上，能够很好地完成红外图像分类分级任务。

Abstract

Scene perception and classification of FLIR images is a key technology in target recognition and of great significance to infrared reconnaissance and guidance. To resolve the problem of scene perception and classification of FLIR images， this study proposes a multi-label infrared image classification algorithm based on weakly supervised learning. First， a multi-label image classification technique is applied to FLIR images， and the images of multiple scenes are annotated using weakly supervised techniques. Infrared image features are extracted using the ResNet-50 network with a residual structure. Second， a CSRA module is introduced to capture the different spatial regions occupied by different classes. The CSRA module can improve the feature expression performance and realize the inference calculation of topological relationships between multiple labels. Finally， the advanced loss function ASL is introduced to solve the imbalance of the number of positive and negative labels in multi-label classification. The advanced loss limits the contribution of negative samples to the loss function and focuses attention on the positive samples during training. An experiment shows that the algorithm has good adaptability and accuracy， and the accuracy can exceed 90%. The algorithm can be used to perform multi-label classification with high accuracy and adaptability.

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references

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