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Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

Based on the influence of dark obstacles caused by insufficient light in an underground mine on the driving safety of an electric locomotive. This paper proposes an improved YOLOX target detection algorithm to effectively identify and classify the track obstacles of the unmanned electric mine locomotive. On the basis of the YOLOX target detection network, the CBAM attention module is added to the CSPDarket and the FPN part of the feature pyramid, and the loss function of YOLO head part is replaced by SIOU. The collected image data of track obstacles of electric locomotive under different lighting conditions are used as the training set. The Pytorch deep learning framework is used to construct an object detection model for training and verification. Experiments show that the average accuracy and recall rate of the improved YOLOX underground electric locomotive track obstacle detection model can reach 93.05% and 88.29%, and the speed is improved to 45.3 fps. Compared with other target detection models, this model can better realize the accuracy and real-time detection of underground electric locomotive track obstacles. It provides the basis for the intelligence of underground mine transportation equipment.

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Author information

Authors and Affiliations

  1. Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Caiwu Lu, Song Jiang, Di Liu & Sai Zhang

  2. Xi’an Key Laboratory of Intelligent Industrial Sensing Computing and Decision Making, Xi’an, 710055, China

    Fan Ji, Song Jiang, Di Liu & Sai Zhang

  3. Sul Ross State University, Alpine, TX, 79830, USA

    Naixue Xiong

Authors
  1. Caiwu Lu
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  2. Fan Ji
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  3. Naixue Xiong
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  4. Song Jiang
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  5. Di Liu
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  6. Sai Zhang
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Correspondence to Fan Ji .

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Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Lu, C., Ji, F., Xiong, N., Jiang, S., Liu, D., Zhang, S. (2023). Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_22

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28123-5

  • Online ISBN: 978-3-031-28124-2

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