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Research on intelligent risk early warning of open-pit blasting site based on deep learning
Liu XB(柳小波)1,2; Yang, Hangyuan1; Jing HD(荆洪迪)1,2; Sun, Xiaoyu1; Yu, Jianyang1
Department数字工厂研究室
Source PublicationEnergy Sources, Part A: Recovery, Utilization and Environmental Effects
ISSN1556-7036
2021
Pages1-18
Indexed BySCI ; EI
EI Accession number20211610232410
WOS IDWOS:000639757800001
Contribution Rank1
Funding OrganizationProject [51674063] ; Research Fund of National Natural Science Foundation of China.
KeywordBlasting safety open pit mine risk early warning deep learning object detection
Abstract

The safety of blasting site in open-pit mine can be greatly improved by risk early warning. Therefore, an intelligent real-time risk early warning method of blasting site in open-pit mine based on deep learning was proposed in this research. The mobile wireless webcams, H.264 video compression algorithm, and real-time transport protocol were applied to achieve real-time video acquisition and transmission of blasting site in open-pit mine. A single-stage deep neural network DG-YOLOv3 was proposed in this research. DG-YOLOv3 is an improvement of Gaussian YOLOv3, among which Darknet41 is used to improve the model’s detection speed and detection accuracy of small targets. To further improve the performance (i.e., speed and accuracy) of risk early warning, the surveillance videos were first split into pictures by frame. Then, the pictures were processed by weighted average grayscale and contrast limited adaptive histogram equalization. Experiments show that the mean average precision of DG-YOLOv3 proposed in this paper reaches 87.45 and the detection speed reaches 56.82 frames per second, which has better accuracy and speed compared with other algorithms. In addition, DG-YOLOv3 has good robustness in complex scenarios. Based on the detection results, the intelligent real-time risk early warning of the blasting site in open-pit mine is achieved finally.

Language英语
WOS SubjectEnergy & Fuels ; Engineering, Chemical ; Environmental Sciences
WOS Research AreaEnergy & Fuels ; Engineering ; Environmental Sciences & Ecology
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28767
Collection数字工厂研究室
Corresponding AuthorJing HD(荆洪迪)
Affiliation1.Intelligent Mine Research Center, Northeastern University, Shenyang, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Liu XB,Yang, Hangyuan,Jing HD,et al. Research on intelligent risk early warning of open-pit blasting site based on deep learning[J]. Energy Sources, Part A: Recovery, Utilization and Environmental Effects,2021:1-18.
APA Liu XB,Yang, Hangyuan,Jing HD,Sun, Xiaoyu,&Yu, Jianyang.(2021).Research on intelligent risk early warning of open-pit blasting site based on deep learning.Energy Sources, Part A: Recovery, Utilization and Environmental Effects,1-18.
MLA Liu XB,et al."Research on intelligent risk early warning of open-pit blasting site based on deep learning".Energy Sources, Part A: Recovery, Utilization and Environmental Effects (2021):1-18.
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