Research on video AI recognition technology for abnormal state of coal mine belt conveyors

MAO Qinghua; GUO Wenjin; ZHAI Jiao; WANG Rongquan; SHANG Xinmang; LI Shikun; XUE Xusheng

doi:10.13272/j.issn.1671-251x.18134

Volume 49 Issue 9

Sep. 2023

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Journal of Mine Automation > 2023 > 49(9): 36-46. > DOI: 10.13272/j.issn.1671-251x.18134

MAO Qinghua, GUO Wenjin, ZHAI Jiao, et al. Research on video AI recognition technology for abnormal state of coal mine belt conveyors[J]. Journal of Mine Automation，2023，49（9）：36-46. DOI: 10.13272/j.issn.1671-251x.18134

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Research on video AI recognition technology for abnormal state of coal mine belt conveyors

MAO Qinghua^{1, 2,},
GUO Wenjin^{1, 2},
ZHAI Jiao^{1, 2},
WANG Rongquan³,
SHANG Xinmang³,
LI Shikun^{1, 2},
XUE Xusheng^{1, 2}

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an 710054, China
3.
Xi'an Heavy Industry Hancheng Coal Mining Machinery Co., Ltd., Hancheng 715400, China

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Received Date: June 04, 2023
Revised Date: August 29, 2023
Available Online: September 18, 2023

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Abstract

Abstract

Traditional belt conveyor abnormal state recognition uses manual inspection or mechanical comprehensive protection system for detection. The manual inspection is labor-intensive, inefficient, and difficult to accurately detect faults. Mechanical comprehensive protection system is prone to misjudgment and poor recognition effect. The above methods can no longer meet the needs of coal industry intelligence. With the development of machine vision, deep learning, and industrial Ethernet technology, video AI technology has become a research hotspot for intelligent recognition of abnormal states of coal mine belt conveyors. This paper analyzes the current research status of using video AI technology to identify abnormal states of coal mine belt conveyors, such as belt deviation, idler failure, personnel invasion, unsafe behavior of personnel, coal stacking, and foreign objects. It is pointed out that there are three main problems in the current video AI recognition technology for abnormal states of coal mine belt conveyors: long construction time-consumption of video image datasets, low precision of abnormal state recognition, and large time delay in video information transmission. To address the issue of long construction time-consumption of video image datasets, a solution is proposed to strengthen the research on video AI recognition algorithms based on semi supervised, unsupervised, and small sample learning, and to expand the dataset based on generative models. To address the issue of low precision of abnormal state recognition, a solution is proposed to strengthen research on data deblurring methods, and to utilize algorithms such as generative adversarial networks to balance positive and negative samples, and improve AI recognition algorithms. To address the issue of large time delay in video information transmission, a solution is proposed to build a "cloud-edge-end" collaborative video AI recognition system architecture for abnormal states of belt conveyors, and to deploy a high bandwidth and low time delay network communication system. This article looks forward to the development trend of video AI recognition technology for abnormal states of belt conveyors from four aspects: high-performance video AI recognition algorithms, high bandwidth and low time delay video communication technology, "cloud-edge-end" efficient collaborative video AI recognition system, and sound video AI recognition technology standards.

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References (62)

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Research on video AI recognition technology for abnormal state of coal mine belt conveyors

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