Research on Target Detection in Underground Coal Mines Based on Improved YOLOv5

KOU Farong; XIAO Wei; HE Haiyang; CHEN Ruochen

doi:10.11999/JEIT220725

Volume 45 Issue 7

Jul. 2023

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KOU Farong, XIAO Wei, HE Haiyang, CHEN Ruochen. Research on Target Detection in Underground Coal Mines Based on Improved YOLOv5[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2642-2649. doi: 10.11999/JEIT220725

Citation:

KOU Farong, XIAO Wei, HE Haiyang, CHEN Ruochen. Research on Target Detection in Underground Coal Mines Based on Improved YOLOv5[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2642-2649. doi: 10.11999/JEIT220725

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Research on Target Detection in Underground Coal Mines Based on Improved YOLOv5

doi: 10.11999/JEIT220725

College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Funds: The National Natural Science Foundation of China (51775426), Shaanxi Province Science and Technology Program Project (2019JQ-795)

Received Date: 2022-06-02
Rev Recd Date: 2022-11-14

Available Online: 2022-11-19

Publish Date: 2023-07-10

Abstract

Abstract

In view of the underground coal mine environment, which uses mostly infrared cameras to sense the surrounding environment’s temperature, the images formed have the problems of less texture information, more noise, and blurred images. The detection of Underground targets in coal mines using YOLOv5(Ucm-YOLOv5), a neural network for real-time detection of coal mines, is suggested in this document. This network is an improvement on YOLOv5. Firstly, PP-LCNet is used as the backbone network for enhancing the inference speed on the CPU side. Secondly, the Focus module is eliminated, and the shuffle_block module is used to replace the C3 module in YOLOv5, which reduces the computation while removing redundant operations. Finally, the Anchor is optimized while introducing H-swish as the activation function. The experimental results show that Ucm-YOLOv5 has 41% fewer model parameters and an 86% smaller model than YOLOv5. The algorithm has higher detection accuracy in underground coal mines, while the detection speed at the CPU side reaches the real-time detection standard, which meets the working requirements for target detection in underground coal mines.
- Coal mine underground target detection,
- Deep learning,
- YOLOv5

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References(20)

References

[1]	LI Ailing, ZHANG Jixiong, ZHOU Nan, et al. A model for evaluating the production system of an intelligent mine based on unascertained measurement theory[J]. Journal of Intelligent & Fuzzy Systems, 2020, 38(2): 1865–1875. doi: 10.3233/JIFS-190329
[2]	ZHANG Kexue, KANG Lei, CHEN Xuexi, et al. A review of intelligent unmanned mining current situation and development trend[J]. Energies, 2022, 15(2): 513. doi: 10.3390/en15020513
[3]	HE Yunze, DENG Baoyuan, WANG Hongjin, et al. Infrared machine vision and infrared thermography with deep learning: A review[J]. Infrared Physics & Technology, 2021, 116: 103754. doi: 10.1016/j.infrared.2021.103754
[4]	WEI Dong, WANG Zhongbin, SI Lei, et al. Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2022, 236(6): 3058–3072. doi: 10.1177/09544062211030973
[5]	RYU J and KIM S. Data driven proposal and deep learning-based small infrared drone detection[J]. Journal of Institute of Control, Robotics and Systems, 2018, 24(12): 1146–1151. doi: 10.5302/J.ICROS.2018.18.0157
[6]	FAN Tao. Research and realization of video target detection system based on deep learning[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2020, 18(1): 1941010. doi: 10.1142/S0219691319410108
[7]	李宝奇, 黄海宁, 刘纪元, 等. 基于改进SSD的水下光学图像感兴趣目标检测算法研究[J]. 电子与信息学报, 2022, 44(10): 3372–3378. doi: 10.11999/JEIT210761 LI Baoqi, HUANG Haining, LIU Jiyuan, et al. Underwater optical image interested object detection model based on improved SSD[J]. Journal of Electronics &Information Technology, 2022, 44(10): 3372–3378. doi: 10.11999/JEIT210761
[8]	LI Xiaoyu, WANG Shuai, LIU Bin, et al. Improved YOLOv4 network using infrared images for personnel detection in coal mines[J]. Journal of Electronic Imaging, 2022, 31(1): 013017. doi: 10.1117/1.JEI.31.1.013017
[9]	JIANG Daihong, DAI Lei, LI Dan, et al. Moving-object tracking algorithm based on PCA-SIFT and optimization for underground coal mines[J]. IEEE Access, 2019, 7: 35556–35563. doi: 10.1109/ACCESS.2019.2899362
[10]	DU Yuxin, TONG Minming, ZHOU Lingling, et al. Edge detection based on Retinex theory and wavelet multiscale product for mine images[J]. Applied Optics, 2016, 55(34): 9625–9637. doi: 10.1364/AO.55.009625
[11]	QIU Zhi, ZHAO Zuoxi, CHEN Shaoji, et al. Application of an improved YOLOv5 algorithm in real-time detection of foreign objects by ground penetrating radar[J]. Remote Sensing, 2022, 14(8): 1895. doi: 10.3390/RS14081895
[12]	CUI Cheng, GAO Tingquan, WEI Shengyu, et al. PP-LCNet: A lightweight CPU convolutional neural network[J]. arXiv preprint arXiv: 2109.15099, 2021.
[13]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
[14]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
[15]	BOCHKOVSKIY A, WANG C C Y, and LIAO H Y M. YoLOv4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv: 2004.10934, 2020.
[16]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788.
[17]	REDMON J and FARHADI A. YoLOv3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[18]	REDMON J and FARHADI A. YOLO9000: Better, faster, stronger[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 7263–7271.
[19]	TAN Mingxing and LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks[C]. The 36th International Conference on Machine Learning, Long Beach, USA, 2019.
[20]	HOWARD A, SANDLER M, CHEN Bo, et al. Searching for MobileNetV3[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019.