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Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine

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Abstract

Real-time object detection in underground coal mine is a crucial task in the development of AI-assisted supervision systems. Due to the complex environment of the underground coal mine, limited computing resources, and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground pose-varied object detection method named Slim-YOLO-PR_KD has been proposed. By designing an efficient pose-varied attention module (EPA) for the backbone network, providing a receive field block (RFB) module for the neck network, and optimizing the loss function, the underground pose-varied detection model YOLO-PR is obtained, which achieved good accuracy but reduced speed. For YOLO-PR, the study improved the original module by designing RFB_SK, a lightweight C2f_GSG module, a shared parameter detection head and selectively replaced modules to slim down the whole network, resulting in a lightweight detection model Slim-YOLO-PR. By using an attention guided knowledge distillation of underground object detection method and using YOLO-PR as the teacher model, the efficient pose-varied detection model Slim-YOLO-PR_KD for coal mine underground is proposed. The experimental results show that compared with the baseline model, the proposed Slim-YOLO-PR_KD has a faster detection speed, achieving higher detection accuracy while reducing model parameters and computational complexity by 42% and 46% respectively, making it capable of performing real-time underground detection tasks. Compared with other general detection models, Slim-YOLO-PR_KD exhibits excellent performance in real-time pose-varied object detection tasks in complex environments of underground coal mines.

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Data availability

The supporting datasets in this article can be obtained through the following link: https://pan.baidu.com/s/1LpdL3p6kXfkqqE2Umyzh7A?pwd=2024.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 52274160 and 51874300, the National Natural Science Foundation of China-Shanxi Provincial People's Government Coal-Based Low Carbon Joint Fund under Grant U1510115, "Jiangsu Distinguished Professor" project in Jiangsu Province (140923070), and the Fundamental Research Funds for the Central Universities Grants 2023QN1079, and 2024QN11050.

Funding

National Natural Science Foundation of China, 52274160.

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Author notes

  1. Huaxing Mu and Jueting Liu made equal contributions to this paper as co-first authors.

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Huaxing Mu, Jueting Liu, Yanyun Guan, Wei Chen, Tingting Xu & Zehua Wang

  2. Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou, China

    Huaxing Mu, Jueting Liu, Yanyun Guan, Wei Chen, Tingting Xu & Zehua Wang

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  1. Huaxing Mu
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  4. Wei Chen
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Contributions

HM made contributions to theory, methodology, partial experiments, and initial draft writing. JL participated in theoretical argumentation, article review, and final draft writing. YG made contributions to some experiments. WC participated in the revision and polishing of the paper. TX and ZW provided experimental equipment and made certain contributions to chart drawing.

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Correspondence to Wei Chen.

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Mu, H., Liu, J., Guan, Y. et al. Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine. J Real-Time Image Proc 21, 160 (2024). https://doi.org/10.1007/s11554-024-01539-0

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