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Pedestrian detection in infrared image based on depth transfer learning

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Abstract

Because of the difficulty in feature extraction of infrared pedestrian images, the traditional methods of object detection usually make use of the labor to obtain pedestrian features, which suffer from the low-accuracy problem. With the development and the progress of science and technology, deep learning has gradually stepped into the problem of object detection, and achieved good results. In this paper, aiming at the defects of deep convolutional neural network, such as the high cost on training time and slow convergence, a new algorithm of MoblieNet V2(1.4) + SSD infrared image pedestrian detection based on transfer learning is proposed, which adopts a transfer learning method and the Adam optimization algorithm to accelerate network convergence. For the experiments, we augmented the OUS thermal infrared pedestrian dataset and our solution enjoys a higher mAP of 94.8% on the test dataset. The experimental results show that our proposed method has the characteristics of fast convergence, high detection accuracy and short detection time.

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Acknowledgments

This research was funded by the National Natural Science Foundation of China, grant number 6192007, 61462008, 61751213, 61866004; the Key projects of Guangxi Natural Science Foundation, grant number 2018GXNSFDA294001,2018GXNSFDA281009; the Natural Science Foundation of Guangxi, grant number 2018GXNSFAA294050, 2017GXNSFAA198365; 2015 Innovation Team Project of Guangxi University of Science and Technology, grant number gxkjdx201504; Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security, grant number MIMS19-04; Natural Science School-level Project of Software Engineering Institute of Guangzhou, grant number ky202108; Guangxi Postgraduate Education Innovation Project, grant number GKYC202106, GKYC202104, YCSW2021320; College Students’ innovation and Entrepreneurship Project 202110594133, 202110594134.

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Authors and Affiliations

  1. School of Computer Science and Telecommunication Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Zhiwen Wang

  2. School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Jing Feng & Yifeng Zhang

  3. South China Institute of Software Engineering, GU, Guangzhou, 510990, Guangdong, China

    Jing Feng

Authors
  1. Zhiwen Wang
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  2. Jing Feng
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  3. Yifeng Zhang
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Contributions

Conceptualization, J.F and Z.w.W.; methodology, J.F; software, Y.h.W.; validation, J.F,Z.w.W.; formal analysis, J.F; investigation, Y.f.Z; data curation, Y.f.Z; writing—original draft preparation, J.F; writing—review and editing, Z.w.W; visualization, J.F; supervision, Y.f.Z; project administration, Z.w.W; funding acquisition, Z.w.W. All authors have read and agreed to the published version of the manuscript.”

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Correspondence to Zhiwen Wang.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Wang, Z., Feng, J. & Zhang, Y. Pedestrian detection in infrared image based on depth transfer learning. Multimed Tools Appl 81, 39655–39674 (2022). https://doi.org/10.1007/s11042-022-13058-w

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