| Citation: |
ZHOU Yunzhuo，XU Zijing，BI Lin. The recognition of dust emission from open-pit mining roads based on cascaded convolutional networks by deep learning[J]. Coal Science and Technology，2024，52（S2）：312−320. DOI: 10.12438/cst.2023-1476
|
The dust emitted from open-pit mines poses a constant menace to both the health of the workers and the environment. However, the traditional manual decision-making and scheduled watering methods are constrained by delayed dust control and excessive water resource consumption, making it challenging to fulfill the requirements of green mining and environmental protection in mining operations. To address the issue of unmanned dust monitoring, an intelligent dust recognition technology based on deep learning was proposed. This technology introduces an innovative approach for tracking and recognizing dust-emitting areas based on a vehicle tracking algorithm. A cascaded deep learning architecture YRCNet (YOLOv5 Tracking with ResNet-50 Classification Cascade Network) was developed, which consists of two main parts: vehicle dynamic tracking and the concentration classification. The YOLOv5 is employed for real-time tracking and capturing images of the rear of vehicles, which enhances the interference resilience of dust image recognition by implementing initial screening of target presence areas. Following this, an optimized ResNet-50 network is used for the classification and recognition module. It employs spatial pyramid pooling layers integrated with deep convolutional blocks, to enhance dust feature extraction and enable precise classification of dust concentrations into high, medium, and low levels. To train this deep learning model, simulated dust images of different concentration levels are generated using simulation techniques and combined with field-collected mining data to create a well-balanced open-pit mining road dust mixed dataset that meets the requirements for model training. The results show that YRCNet achieves a recognition accuracy of 94.25% on mixed dust dataset, representing a 13.58% improvement over the previous network. The model exhibits generalization across various open-pit mining road scenarios, providing an effective perceptual means for intelligent dust reduction. Additionally, it contributes to the monitoring and enhancement of environmental quality in mining areas, ensuring the health and safety of the workforce.
| [1] |
罗德权. 关于露天矿连续开采工艺的几个问题[J]. 煤炭科学技术,1979,7(6):27−31.
LUO Dequan. Several issues on continuous extraction processes in open-pit mining[J]. Coal Science and Technology,1979,7(6):27−31.
|
| [2] |
陆国荣. 国外露天矿汽车运输路面扬尘防治技术(一)[J]. 国外金属矿山,1991(11):69−78.
LU Guorong. Prevention and control technology of dust on automobile transportation pavement in foreign open pit mines (1)[J]. Foreign Metal Mining Magazine,1991(11):69−78.
|
| [3] |
尤文顺. 神东矿区粉尘控制技术及防治措施[J]. 煤炭科学技术,2005,33(3):13−16.
YOU Wenshun. Dust control technology and prevention measures in Shendong mining area[J]. Coal Science and Technology,2005,33(3):13−16.
|
| [4] |
程卫民,周刚,陈连军,等. 我国煤矿粉尘防治理论与技术20年研究进展及展望[J]. 煤炭科学技术,2020,48(2):1−20.
CHENG Weimin,ZHOU Gang,CHEN Lianjun,et al. Research progress and prospect of dust control theory and technology in China’s coal mines in the past 20 years[J]. Coal Science and Technology,2020,48(2):1−20.
|
| [5] |
池鲁生. 煤矿防尘降尘工作的主要经验[J]. 煤炭科学技术,1992,20(10):52−54.
CHI Lusheng. Main experience of dust control and dust reduction in coal mines[J]. Coal Science and Technology,1992,20(10):52−54.
|
| [6] |
金龙哲,蒋仲安,张文平,等. 粉尘分散度直接测定方法的研究[J]. 煤炭科学技术,1998,26(4):8−10.
JIN Longzhe,JIANG Zhongan,ZHANG Wenping,et al. Study on direct determination method of dust dispersion[J]. Coal Science and Technology,1998,26(4):8−10.
|
| [7] |
施无右. 我国粉尘检测技术与国外的差距及发展方向[J]. 工业安全与防尘,1989,15(2):31−34,23.
SHI Wuyou. The gap and development direction of dust detection technology between China and foreign countries[J]. Industrial Safety and Environmental Protection,1989,15(2):31−34,23.
|
| [8] |
KORI B K,AGRAWAL D H. Dust monitoring systems and health hazards in coal mining A review[J]. Journal of Trend in Scientific Research and Development,2021,5(3):172−177.
|
| [9] |
张瑞新,毛善君,赵红泽,等. 智慧露天矿山建设基本框架及体系设计[J]. 煤炭科学技术,2019,47(10):1−23.
ZHANG Ruixin,MAO Shanjun,ZHAO Hongze,et al. Framework and structure design of system construction for intelligent open-pit mine[J]. Coal Science and Technology,2019,47(10):1−23.
|
| [10] |
KANG J S,KANG J,KIM J J,et al. Neural architecture search survey:A computer vision perspective[J]. Sensors,2023,23(3):1713. doi: 10.3390/s23031713
|
| [11] |
ALBATAYNEH O,FORSLÖF L,KSAIBATI K. Developing and validating an image processing algorithm for evaluating gravel road dust[J]. International Journal of Pavement Research and Technology,2019,12(3):288−296. doi: 10.1007/s42947-019-0035-y
|
| [12] |
王艳,张游杰. 基于改进残差网络的扬尘图像识别方法[J]. 计算机系统应用,2021,30(5):202−207.
WANG Yan,ZHANG Youjie. Dust image recognition method based on improved residual network[J]. Computer Systems & Applications,2021,30(5):202−207.
|
| [13] |
KO K K,JUNG E S. Improving air pollution prediction system through multimodal deep learning model optimization[J]. Applied Sciences,2022,12(20):10405. doi: 10.3390/app122010405
|
| [14] |
程学珍,赵振国,刘兴军,等. 基于YOLOv4算法的煤矿井下粉尘检测方法[J]. 实验室研究与探索,2022,41(3):14−18.
CHENG Xuezhen,ZHAO Zhenguo,LIU Xingjun,et al. Dust detection method for underground coal mines based on YOLOv4 algoritm[J]. Research and Exploration in Laboratory,2022,41(3):14−18.
|
| [15] |
李海滨,孙远,张文明,等. 基于YOLOv4-tiny的溜筒卸料煤尘检测方法[J]. 光电工程,2021,48(6):73−86.
LI Haibin,SUN Yuan,ZHANG Wenming,et al. The detection method for coal dust caused by chute discharge based on YOLOv4-tiny[J]. Opto-Electronic Engineering,2021,48(6):73−86.
|
| [16] |
金立生,华强,郭柏苍,等. 基于优化DeepSort的前方车辆多目标跟踪[J]. 浙江大学学报(工学版),2021,55(6):1056−1064.
JIN Lisheng,HUA Qiang,GUO Baicang,et al. Multi-target tracking of vehicles based on optimized DeepSort[J]. Journal of Zhejiang University (Engineering Science),2021,55(6):1056−1064.
|
| [17] |
SHAFIQ M,GU Zhaoquan. Deep residual learning for image recognition:A survey[J]. Applied Sciences,2022,12(18):8972. doi: 10.3390/app12188972
|
| [18] |
HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904−1916. doi: 10.1109/TPAMI.2015.2389824
|
| [19] |
XIONG Ruoxin,TANG Pingbo. Machine learning using synthetic images for detecting dust emissions on construction sites[J]. Smart and Sustainable Built Environment,2021,10(3):487−503. doi: 10.1108/SASBE-04-2021-0066
|
| [20] |
张雪兰,申华. 虚拟战场中车尾扬尘的实时仿真方法[J]. 火力与指挥控制,2006,31(1):63−66.
ZHANG Xuelan,SHEN Hua. Real-time simulation of dust behavior in virtual battlefield[J]. Fire Control and Command Control,2006,31(1):63−66.
|
| [21] |
TASIĆ V,JOVAŠEVIĆ-STOJANOVIĆ M,VARDOULAKIS S,et al. Comparative assessment of a real-time particle monitor against the reference gravimetric method for PM10 and PM2.5 in indoor air[J]. Atmospheric Environment,2012,54:358−364. doi: 10.1016/j.atmosenv.2012.02.030
|
| [22] |
XIAO Jieying,LIU Juan,ZHAO Wenxia,et al. Road dust emission spatial characteristics of Shijiazhuang in 2014 spring[C]//Proceedings of the 2015 2nd International Conference on Machinery,Materials Engineering,Chemical Engineering and Biotechnology. Amsterdam:Atlantis Press,2015:624-627.
|
| [23] |
陈锋. 基于图像法的粉尘浓度检测设计与研究[D]. 杭州:中国计量大学,2019.
CHEN Feng. Design and research of dust concentration detection based on image method[D]. Hangzhou:China University of Metrology,2019.
|
| [24] |
郭永存,王希,何磊,等. 基于TW-RN优化CNN的煤矸识别方法研究[J]. 煤炭科学技术,2022,50(1):228−236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023
GUO Yongcun,WANG Xi,HE Lei,et al. Research on coal and gangue recognition method based on TW-RN optimized CNN[J]. Coal Science and Technology,2022,50(1):228−236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: