The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm

Li, Jie; Miao, Changyun

doi:10.1016/j.ijleo.2016.05.111

Cited by 49 publications

(28 citation statements)

References 4 publications

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“…AP is presented in formula (10). TAfps is shown in formula (11). " " is the recall rate of the nth experiment；" " is the minimum recall rate of a group of experiments；" " is the maximum recall rate of a group of experiments； " " is the precision rate of the nth experiment；" " is the minimum precision rate of a group of experiments；" " is the maximum precision rate of a group of experiments；" " is the average FPS obtained in the nth experiment。 The performance index of the detection method based on ADCN and SVM is shown in Table 7.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…The existing damage detection methods for conveyor belt based on machine vision include: Chen et al proposed the SVM-based monitoring technology to detect the damage of mining conveyor belt [10]. Li et al used the optimized SSR algorithm to detect the damage of conveyor belt [11]. Yu et al proposed a dual-band machine vision method based on the combination of medium infrared light and long infrared light to detect belt damage [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Qiao

Pang

et al. 2021

IEEE Sensors J.

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Belt conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of conveyor belt damage detection based on ADCN (Adaptive Deep Convolutional Network). A deep convolution network with unique adaptability is built to extract the different scale features of visible light image of conveyor belt damage, and the target is classified and located in the form of anchor boxes. A data set with data diversity is collected according to the actual working conditions of the conveyor belt. After training and regression, the ADCN model can perfectly capture and classify the damaged target in the video of the conveyor running. Compared with the SVM based method, the method based on ADCN can better meet the realtime and reliability requirements of belt damage detection, and it has the positioning function which SVM does not have.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Qiao

Pang

et al. 2021

IEEE Sensors J.

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“…e proposed algorithm is composed of the following three steps: (1) given the radius r i and the vertical scaling factor s j , the coordinate (w, z) of an arbitrary point on the base arc is determined as…”

Section: Idler Outer Edge Detection Algorithmmentioning

confidence: 99%

“…Belt conveyor is continuous transportation equipment in modern production with the advantages of large capacity, being suitable for long distance, low freight, high efficiency, stable operation, convenient loading and unloading, being suitable for bulk material transportation, etc. It has become one of the three main industrial conveyances together with automobile and train and has been widely used in coal, mines, ports, electric power, metallurgy, chemical industry, and other fields [1]. e operating condition of the belt conveyor is harsh, and the deviation fault occurs due to improper installation and adjustment, manufacturing errors, bearing idler failure, uneven distribution of materials, adhesion of slime to the driving pulley and the bend pulley, etc.…”

Section: Introductionmentioning

confidence: 99%

Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning

Liu

Miao

et al. 2021

Complexity

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The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest (ROI) containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD (OM-SSD). Secondly, Hough line transform algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree (DD) of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor.

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“…Researchers have designed several methods associated with longitudinal tear detection. Li and Miao proposed a discriminating method based on improved SSR algorithm for realising real-time tear detection [8]. Before that, Xue [9] researched sensor-based comprehensive detection method for belt longitudinal tear.…”

Section: Introductionmentioning

confidence: 99%

Research on conditional characteristics vision real‐time detection system for conveyor belt longitudinal tear

Qiao

Pang

et al. 2017

IET Science, Measurement & Technology

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Abstract:Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.

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The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm

Cited by 49 publications

References 4 publications

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Research On ADCN Method for Damage Detection of Mining Conveyor Belt

Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning

Research on conditional characteristics vision real‐time detection system for conveyor belt longitudinal tear

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