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Rep-YOLO: an efficient detection method for mine personnel

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Abstract

The detection of underground personnel is one of the key technologies in computer vision. However, this detection technique is susceptible to complex environments, resulting in low accuracy and slow speed. To accurately detect underground coal mine operators in complex environments, we combine the underground image features with K-means++ clustering anchor frames and propose a new Re-parameterization YOLO (Rep-YOLO) detection algorithm. First, the Criss-Cross-Vertical with Channel Attention (CVCA) mechanism is introduced at the end of the network to capture the Long-Range Dependencies (LRDs) in the image. This mechanism also emphasizes the significance of different channels to enhance image processing performance and improve the representation ability of the model. Second, the new Deep Extraction of Re-parameterization (DER) backbone network is designed, which adopts the re-parameterization structure to reduce the number of parameters and computation of the model. Additionally, each DER-block fuses different scales of features to enhance the accuracy of the model’s detection capabilities. Finally, Rep-YOLO is optimized using a slim-neck structure, which reduces the complexity of the Rep-YOLO while maintaining sufficient accuracy. The results showed that the Rep-YOLO model proposed in this paper achieved an accuracy of \(87.5\%\), a recall rate of \(77.2\%\), an Average Precision (AP) of \(83.1\%\), and a Frame Per Second (FPS) of 71.9. Compared to eight different models, the recall, AP50, and FPS of the Rep-YOLO model were improved. The research shows that the Rep-YOLO model can provide a real-time and efficient method for mine personnel detection. Source code is released in https://github.com/DrLSB/Rep-YOLO.

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Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available due [their containing information that could compromise the privacy of research participants] but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by Grants from the National Natural Science Foundation of China (52174198).

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Authors and Affiliations

  1. College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

    Xiaoqiang Shao, Shibo Liu, Xin Li, Zhiyue Lyu & Hao Li

  2. Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi’an, 710054, China

    Xiaoqiang Shao, Shibo Liu, Xin Li, Zhiyue Lyu & Hao Li

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  1. Xiaoqiang Shao
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Contributions

XS contributed to conceptualization, methodology, and writing—original draft preparation. SL was involved in conceptualization, methodology, writing—original draft preparation, and writing—reviewing and editing. XL contributed to conceptualization and data curation. ZL contributed to conceptualization and writing-reviewing. HL contributed to conceptualization and methodology.

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Correspondence to Shibo Liu.

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Shao, X., Liu, S., Li, X. et al. Rep-YOLO: an efficient detection method for mine personnel. J Real-Time Image Proc 21, 28 (2024). https://doi.org/10.1007/s11554-023-01407-3

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