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Multi-target detection method for driving area of mine driverless rail locomotives based on lightweight network

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Abstract

Existing target detection algorithms have limitations in complex mine environments like low illumination, small targets, background interference, occlusion, and motion blur. Also, their complex network structures and large parameter volumes can't meet real-time detection needs of edge devices. Thus, a lightweight network-based multi-target detection method for mine driverless rail locomotive driving areas was proposed. A dataset of seven target images (electric locomotives, miners, etc.) in five scenarios (normal & low illumination, etc.) was constructed. Based on YOLOv5s, improvements were made: adding a small target detection layer to enhance small target detection; using the GhostBottleNeck module to replace BottleNeck in C3 to build C3Ghost, reducing calculation and parameters and compensating for the added layer; introducing the SimAM attention mechanism to focus on targets and suppress interference; replacing CIoU with SIoU loss function to speed up convergence. Experimental results show the proposed lightweight network cuts parameters by 12.3%, boosts mAP by 1.7%, and is more suitable for multi-object detection in mine rail locomotive driving areas.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded by Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology under Grant No.2023yjrc95, the Open Fund of Anhui Intelligent Mine Technology and Equipment Engineering Research Center under Grant No. AIMTEERC202405, the National Natural Science Foundation of China Project under Grant No. 52274153.

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Authors and Affiliations

  1. Anhui Intelligent Mine Technology and Equipment Engineering Research Center, Anhui University of Science and Technology, Huainan, China

    Wenshan Wang & Kun Hu

  2. School of Mechatronics Engineering, Anhui University of Science and Technology, Huainan, China

    Wenshan Wang, Kun Hu & Hao Jiang

  3. Collaborative Innovation Center for Mining Intelligent Technology and Equipment Co-Built by Province and Ministry, Anhui University of Science and Technology, Huainan, China

    Wenshan Wang & Kun Hu

Authors
  1. Wenshan Wang
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  2. Kun Hu
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Contributions

Wenshan Wang conceived the idea. Wenshan Wang and Kun Hu performed the data analyses and wrote the manuscript. Hao Jiang edited the manuscript. All authors discussed the results and revised the manuscript.

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Correspondence to Wenshan Wang.

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Wang, W., Hu, K. & Jiang, H. Multi-target detection method for driving area of mine driverless rail locomotives based on lightweight network. SIViP 19, 276 (2025). https://doi.org/10.1007/s11760-025-03883-5

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  • DOI: https://doi.org/10.1007/s11760-025-03883-5

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