Real-time measuring and warning of surrounding rock dynamic deformation and failure in deep roadway based on machine vision method

Xu, Jiankun; Wang, Enyuan; Zhou, Rui

doi:10.1016/j.measurement.2019.107028

Cited by 35 publications

(13 citation statements)

References 23 publications

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“…To prevent the deformation of roads and collapses in underground coal mining, Xu et al constructed an automated monitoring and early warning system. The system captures roads’ tendency to deform, the deformation, and the damage to surrounding rocks, to warn about the instability of the latter [ 16 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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“…The design concept of DDMITS was to collect the video footage of the measured plane using a camera and upload it to the computer 38 . Then, automatic identification of the tracking targets on the images was conducted, obtaining the position coordinates of the reference point.…”

Section: Design Of the Ddmitsmentioning

confidence: 99%

“…The dynamic deformation and fracture of deep roadway surrounding rock has the characteristics of regionality and continuity, which is due to the reason that majority roadway deformation monitoring methods are discontinuous in nature 38 . The interval time between the two measurements is long.…”

Section: Introductionmentioning

confidence: 99%

Dynamic deformation and fracture characteristics of a deep roadway surrounding rock based on the machine vision monitoring method

Zhou

Wang

et al. 2020

Energy Science & Engineering

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The deformation and fracture of surrounding rock in deep underground engineering have the characteristics of time mutation and space continuity. In order to monitor and provide early warning of the dynamic deformation and fracture characteristics of deep surrounding rock, a real‐time digital displacement measuring instrument was developed in this paper. The proposed device represented a kind of surrounding rock deformation and fracture monitoring method, which had the continuous and real‐time advantages. Based upon the simulation experiments, the dynamic deformation and fracture of deep roadway surrounding rock were measured synchronously using the digital displacement measuring instrument testing system (DDMITS) and the V‐STARS measurement system. Comparative measurement results indicated that the DDMITS had a higher reliability. The deformation of roadway surrounding rock gradually increased with the increase of loading. The roof deformation and caving changing patterns in coal seam mining were studied using similar simulation experiments based on the DDMITS. The vertical displacement response of rock strata was more obvious than the horizontal displacement. There was a sharp increase in the vertical displacement during the collapse of the rock strata. Additionally, the vertical displacement velocity showed fluctuations. Meanwhile, the fluctuations in the vertical displacement acceleration increased significantly. The DDMITS could achieve full‐field measurement and the noncontact measurement of three‐dimensional deformation.

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“…Although the monitoring and early warning technologies of heading face are not mature enough, it has also formed some achievements in recent years. e commonly used early warning technologies include AE monitoring [27], coal stress monitoring [28], electromagnetic radiation [29], and conventional pressure monitoring (displacement measurement [30] and bolt force measurement [31,32]). AE monitoring uses AE events as objects for early warning, and some successful cases have been achieved [33,34]; however, the energy of AE events is usually very small and is easy to be interfered by construction equipment or electromagnetic, which may result in inaccurate basic statistics.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Valuable Microseismic Events in Heading Face of an Underground Coal Mine Using Microseismic System

Zhang

Huo

et al. 2021

Shock and Vibration

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Rock burst monitoring of heading face is a weak aspect of rock burst monitoring in China; acoustic emission (AE) monitoring is one of the few monitoring technologies used in heading face, but its target signals are small energy events which are easy to be disturbed. Researchers usually focus on the weak AE events but ignore the microseismic (MS) events (different from AE event and caused by a larger scale of coal fracture), while this kind of events can also reflect the pressure situation of heading face and have higher energy value which may become a better indicator for rock burst monitoring of heading face. So, the basic characteristics of MS events in heading face are studied based on a running vibration signal acquisition system, including the occurrence position, main frequency range, maximum amplitude (MA) range, event duration, and relationship with geological structure. This paper provides a development basis of the monitoring method for rock burst monitoring of heading face by using MS events.

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Real-time measuring and warning of surrounding rock dynamic deformation and failure in deep roadway based on machine vision method

Cited by 35 publications

References 23 publications

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Dynamic deformation and fracture characteristics of a deep roadway surrounding rock based on the machine vision monitoring method

Characteristics of Valuable Microseismic Events in Heading Face of an Underground Coal Mine Using Microseismic System

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