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Infrared target detection using deep learning algorithms

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Abstract

Automatic target recognition is critical in infrared imaging guidance. However, since the diversity of the environment, the infrared data are often complex and difficult to analyze accurately. We proposed a deep learning infrared target detection framework based on transposed convolution and fusion modules (TF-SSD). Compared with one-stage detector YOLOv5 and two-stage detector Faster R-CNN, TF-SSD has three highlights: (1) using the visualization method to revise the network structure and improve the training efficiency; (2) using transposed convolution operation to increase feature extraction ability and detection efficiency; (3) using multi-scale feature fusion models to realize the skip connection between the high-level network and the low-level network. Experimental results on our dataset of six common flight attitudes demonstrate that the maximum average precision (AP) is 90.9%, the minimum average precision is 79.8%, and the overall mean average precision (mAP) is 85.7%. It is confirmed that our proposed TF-SSD system can effectively recognize infrared targets.

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Acknowledgements

We would like to thank the anonymous reviewers of this paper for their valuable and constructive comments.

Funding

This work is supported by Aeronautical Science Foundation of China (20170142002) and Natural Science Foundation of Henan Province (162300410095).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Hainan University, Haikou, 570228, China

    Laixiang Xu

  2. School of Information Engineering, Luohe Vocational Technology College, Luohe, 462000, China

    Bingxu Cao

  3. Roasting No.2 Branch, Xinjiang Shenhuo Carbon Products Co., Ltd, Fukang, 831500, China

    Peng Xu

  4. Henan Sui County People’s Hospital, The First Affiliated Hospital of Zhengzhou University, Shangqiu First people’s Hospital, Shangqiu, 476000, China

    Fengjie Zhao

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  1. Laixiang Xu
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  2. Bingxu Cao
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  3. Peng Xu
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  4. Fengjie Zhao
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Contributions

LX and BC designed the framework of models. PX and LX studied conception. BC and LX established the experimental platform. LX collected experimental data. LX, FZ, and PX summarized and discussed experimental results. LX and BC wrote the main manuscript text. LX and FZ prepared Figs. 1–7 and Tables 1–2. All authors reviewed the manuscript.

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Correspondence to Laixiang Xu.

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Xu, L., Cao, B., Xu, P. et al. Infrared target detection using deep learning algorithms. SIViP 17, 3993–4000 (2023). https://doi.org/10.1007/s11760-023-02629-5

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  • DOI: https://doi.org/10.1007/s11760-023-02629-5

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