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Infrared target recognition with deep learning algorithms

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Abstract

Infrared automatic target recognition (ATR) technology still is a challenging problem in military applications. In recent years, convolutional neural networks (CNNs) models have already led to breakthrough developments in object detection and target recognition. However, the complex environment and the bad weather caused the poor texture information and the weak background of infrared imaging. It’s difficult to use standard CNNs to perform accurate feature extraction and target classification. To overcome these shortcomings, we propose a novel deep learning framework, composed of the multi-kernel transformation and the Alpha-Beta divergence. The multi-kernel transformation operation is designed between convolutional layers and pooling layers to increase the confidence of feature extraction. The Alpha-Beta divergence is used as a penalty term to re-encode the output neurons of improved CNNs, which can promote the recognition performance of the entire network. Furthermore, comprehensive theoretical analysis and extensive experiments are confirmed that our proposed framework outperforms ResNet, VGG-19, DenseNet, and the different combinations of models in many aspects, such as short time-consuming, high accuracy, and strong robustness. Our approach yields a maximum accuracy score of 98.43% on our dataset. Meanwhile, we use the OKTAL-SE-based synthetic database and the SENSIAC dataset to verify our models. Experimental results demonstrate the maximum average accuracy is 97.16%, it is feasible and effective for infrared target recognition.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This work is supported by National Key Research and Development Program of China (2018YFC1407505), Aeronautical Science Foundation of China (20170142002), Natural Science Foundation of Henan Province (162300410095), Natural Science Foundation of Hainan Province (119MS001), and the scientific research fund of Hainan University (No. kyqd1653).

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Authors and Affiliations

  1. School of Information and Communication Engineering and School of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, Hainan University, Hainan, 570228, China

    Laixiang Xu

  2. Department of Pediatrics, Henan Sui Xian People’s Hospital and The First Affiliated Hospital of Zhengzhou University, Shangqiu First People’s Hospital, Shangqiu, 476000, China

    Fengjie Zhao

  3. Roasting No.2 Branch, Xinjiang Shen Huo Garbon Co., Ltd, Fukang, 831500, China

    Peng Xu

  4. School of Information Engineering, Luohe Vocational Technology College, Henan University of Technology Luohe Institute of Technology, Luohe, 462000, China

    Bingxu Cao

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  1. Laixiang Xu
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  2. Fengjie Zhao
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  3. Peng Xu
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Correspondence to Laixiang Xu.

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Xu, L., Zhao, F., Xu, P. et al. Infrared target recognition with deep learning algorithms. Multimed Tools Appl 82, 17213–17230 (2023). https://doi.org/10.1007/s11042-022-14142-x

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  • DOI: https://doi.org/10.1007/s11042-022-14142-x

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