Home Journals Articles Updates About
CN
image
收藏切换
Algorithm for Detecting Free Space in Underground Mine Tunnels for Autonomous Vehicles
收藏切换
PDF
Zhijun Chen1, 2, Chaowei Wang1, 2, Chaozhong Wu1, 3, 4, Chuang Qian1, Huaizhu Wu5, Guangjun Shen5
Automotive Engineering | 2024, 46(11) : 2017 - 2027
Less
收藏切换
Automotive Engineering | 2024, 46(11): 2017-2027
• Feature Topic：Key Technologies on Intelligent and Connected Vehicles •
Algorithm for Detecting Free Space in Underground Mine Tunnels for Autonomous Vehicles
Full
Zhijun Chen1, 2, Chaowei Wang1, 2, Chaozhong Wu1, 3, 4, Chuang Qian1, Huaizhu Wu5, Guangjun Shen5
Affiliations
  • 1. Intelligent Transportation Systems Research Center，Wuhan University of Technology，Wuhan 430063
  • 2. School of Computer Science and Artificial Intelligence，Wuhan University of Technology，Wuhan 430070
  • 3. Engineering Research Center of Transportation Information and Safety，Ministry of Education，Wuhan 430063
  • 4. Hubei University of Arts and Science，Xiangyang 441053
  • 5. Dongfeng Commercial Vehicle Technical Center，Dongfeng Commercial Vehicle Co. ，Ltd. ，Wuhan 430056
Published: 2024-11-25 doi: 10.19562/j.chinasae.qcgc.2024.11.008
Outline
收藏切换

The detection of free space in underground mine tunnels is the key sensing technology for underground mining autonomous driving systems. However，the characteristics of low illumination and complex working environment inside the tunnels bring great challenges to this task. In view of this，in this paper an algorithm for detecting free space in underground mine tunnels is proposed. Firstly，a dual-branch feature extraction backbone network is proposed to solve the problem of difficulty in extracting image features caused by the degradation of tunnel details. Secondly，for the problem of incomplete detection of drivable areas in underground mining tunnels，an adaptive multi-scale atrous spatial pyramid pooling feature enhancement module is proposed. Finally，a dual-branch channel attention mechanism fusion module is developed to solve the problem of inaccurate boundary extraction in the underground mine tunnels. The experiments are conducted on a self-made dataset specifically designed for underground mine tunnels. The results show that the proposed algorithm surpasses other existing methods such as Deeplabv3+，UNet，DDRNet-23，and PIDNet，with an increase of 2.07，2.39，1.87，and 1.92 percentage points in terms of MIoU scores，and 1.78，2.45，1.84，and 1.86 in terms of mAcc scores，respectively. The effectiveness of the proposed algorithm has been validated through its successful application in real mine tunnel scenarios，particularly for underground mining autonomous driving vehicles.

autonomous driving  /  unmanned mine truck  /  free space detection  /  semantic segmentation
Zhijun Chen, Chaowei Wang, Chaozhong Wu, Chuang Qian, Huaizhu Wu, Guangjun Shen. Algorithm for Detecting Free Space in Underground Mine Tunnels for Autonomous Vehicles[J]. Automotive Engineering, 2024 , 46 (11) : 2017 -2027 . DOI: 10.19562/j.chinasae.qcgc.2024.11.008
Appendix
Less
Year 2024 volume 46 Issue 11
PDF
319
135
Cite this Article
BibTeX
Article Info
doi: 10.19562/j.chinasae.qcgc.2024.11.008
  • Receive Date：2024-06-07
  • Online Date：2025-07-21
  • Published：2024-11-25
Article Data
Affiliations
History
  • Received：2024-06-07
  • Revised：2024-07-09
Funding
Affiliations
    1. Intelligent Transportation Systems Research Center，Wuhan University of Technology，Wuhan 430063
    2. School of Computer Science and Artificial Intelligence，Wuhan University of Technology，Wuhan 430070
    3. Engineering Research Center of Transportation Information and Safety，Ministry of Education，Wuhan 430063
    4. Hubei University of Arts and Science，Xiangyang 441053
    5. Dongfeng Commercial Vehicle Technical Center，Dongfeng Commercial Vehicle Co. ，Ltd. ，Wuhan 430056
References
Share
https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.11.008
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House