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

Qiao, Tiezhu; Li, Xinyu; Pang, Yusong; Lü, Yuxiang Yuxiang; Wang, Feng; Jin, Baoquan

doi:10.1049/iet-smt.2017.0100

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…However, the frequency domain is limited to tear detection and lacks generalization ability. Qiao et al [16][17][18][19][20] proposed a Harris corner point detection algorithm with filtering function that helps to make it possible to eliminate the influence of pseudo-corners in feature recognition. Combined with a Hough transform, the original image is divided into a highlight area and a dent area.…”

Section: Related Workmentioning

confidence: 99%

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Hao¹,

Meng²,

Zhang³

et al. 2021

Computers, Materials &Amp; Continua

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In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision.To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated by the generator has a certain degree of randomness. Here, a small number of labeled belt images are taken as conditions and added them to the generator and discriminator, so the generator can generate images with the characteristics of belt damage under the aforementioned conditions. Moreover, because the discriminator cannot identify multiple types of damage, the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities, and it can accurately classify cracks, scratches, and tears. To avoid the features learned incompletely, skiplayer connection is adopted in the generator and discriminator. This not only can minimize the loss of features, but also improves the convergence speed. Compared with other algorithms, experimental results show that the loss value of the generator and discriminator is the least. Moreover, its convergence speed is faster, and the mean average precision of the proposed algorithm is up to 96.2%, which is at least 6% higher than that of other algorithms.

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Section: Related Workmentioning

confidence: 99%

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Hao¹,

Meng²,

Zhang³

et al. 2021

Computers, Materials &Amp; Continua

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“…In order to highlight the crack features more obviously, auxiliary equipment is applied to the crack detection of conveyor belt. Qiao et al [15] use laser and area light sources to enhance the contrast between conditional features and conveyor belt surface. They propose a false corner filtration and single-point feature identification method to detect longitudinal tears.…”

Section: Introductionmentioning

confidence: 99%

Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography

et al. 2021

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For a belt conveyor, a belt with mini-cracks influences the safety of the operation of the belt conveyor devices significantly. The lumpy material rubbing against a belt with mini-cracks will lead to detrimental tears on the belt while the conveyor works at high speeds. In order to detect mini-cracks in a timely manner, before the occurrence of large tears, a new mini-crack detection method for conveyor belts based on laser excited thermography is proposed. A laser beam is applied to generate a heat wave on the belt surface, so the corresponding thermal images can be recorded by an infrared camera. Then, an algorithm for extracting and fusing the features of mini-crack belt’s thermal images is firstly proposed by using time sequence analysis. It could transform multiple sequential thermal images into a single thermal image according to the frame difference method, so that the infrared thermal response of the mini-crack over time could be recorded quickly. To reveal the features of mini-crack, the power function image enhancement algorithm is proposed. After that, the threshold segmentation and Hough transform algorithm are used to determine the size of the mini-crack. The experimental results show that the mini-cracks with length of 50–140 mm, width of 1–10 mm, and depth of 5 mm can be successfully detected, and the detection accuracy could reach 96%. The detection results of different sizes of mini-crack show that the laser excited thermography has high damage sensitivity (1 mm diameter mini-crack detection), high accuracy (96%), lower power consumption (15 W laser excitation), and high SNR (signal-to-noise ratio).

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“…In the literature, there are many examples describing the diagnostics of the conveyor, mainly using stationary measuring stations that perform diagnostics using: laser [10,11], vibrations [12][13][14], thermovision [15], acoustics [16,17], radio-frequency identification (RFID) [18], and cameras [19,20]. Given a research problem regarding the damage presented, the state-of-the-art approaches focus mainly on cameras, with single cases of using other technologies, such as lasers [21].…”

Section: Introductionmentioning

confidence: 99%

Procedures of Detecting Damage to a Conveyor Belt with Use of an Inspection Legged Robot for Deep Mine Infrastructure

et al. 2021

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Conveying systems are responsible for a large part of continuous horizontal transportation in underground mines. The total length of a conveyor network can reach hundreds of kilometers, while a single conveyor usually has a route length of about 0.5–2 km. The belt is a critical and one of the most costly components of the conveyor, and damage to it can result in long unexpected stoppages of production. This is why proper monitoring of conveyor belts is crucial for continuous operation. In this article, algorithms for the detection of potential damage to a conveyor belt are described. The algorithms for analysis used video recordings of a moving belt conveyor, which, in case the of hazardous conditions of deep mines, can be collected, for example, by a legged autonomous inspection robot. The video was then analyzed frame by frame. In this article, algorithms for edge damage detection, belt deviation, and conveyor load estimation are described. The main goal of the research was to find a potential application for image recognition to detect damage to conveyor belts in mines.

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

Cited by 13 publications

References 18 publications

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography

Procedures of Detecting Damage to a Conveyor Belt with Use of an Inspection Legged Robot for Deep Mine Infrastructure

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