On-line conveyor belts inspection based on machine vision

Yang, Yanli; Miao, Changyun; Li, Xianguo; Mei, Xiuzhuang

doi:10.1016/j.ijleo.2014.07.070

Cited by 80 publications

(36 citation statements)

References 6 publications

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“…When all employed bees complete the search for new food sources, the fitness values of new food sources are calculated and compared to the old ones according to the greedy selection mechanism of The operation procedure of ABC algorithm --Initialization phase --(1) Initialize the population of solutions and assign the population to employed bees (2) while (cycle = MAXcycles) do --Employed bee phase -- (3) for = 1 to SN do (4) Produce a new solution V for employed bees and calculate its fitness value (5) Apply the greedy selection mechanism between V and , select the better one (6) If the solution does not update, the non-updated number trial = trial + 1; otherwise trial = 0 (7) end for --Onlooker bee phase -- (8) Calculate the selection probability (9) = 0, = 1 (10) while ( < ) do (11) if random < then (12) = + 1 (13) Produce a new solution V for onlooker bee of the solution and calculate its fitness value (14) Apply the greedy selection mechanism between V and , select the better one (15) If the solution does not update, trial = trial + 1; otherwise trial = 0 (16) end if (17) = + 1 (18) If = + 1, = 1 (19) end while ( = ) --Scout bee phase -- (20) if trial > LIMIT then (21) Replace with a new random solution V (22) end if (23) Memorize the current optimal solution (24) cycle = cycle + 1 (25) end while (cycle = MAXcycles) Algorithm 1: The pseudocode of ABC algorithm. the amounts and the positions of food sources with onlooker bees.…”

Section: Artificial Colony Bee (Abc)mentioning

confidence: 99%

“…As a noncontact method, the machine vision can capture the rich image-based information of detected regions in real time by the CCD camera deployed on the equipment. Therefore, image-based fault detection can satisfy the needs of modem industrial production including manufacturing processes [2], electrified railways [3], and defect detection [4].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

Chen

Zhang

Wang

et al. 2015

Mathematical Problems in Engineering

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Fault detection has become extremely important in industrial production so that numerous potential losses caused from equipment failures could be saved. As a noncontact method, machine vision can satisfy the needs of real-time fault monitoring. However, image-based fault features often have the characteristics of high-dimensionality and redundant correlation. To optimize feature subsets and SVM parameters, this paper presents an enhanced artificial bee colony-based support vector machine (EABC-SVM) approach. The method is applied to the image-based fault detection for the conveyor belt. To improve the optimized capability of original ABC, the EABC algorithm introduces two enhanced strategies including the Cat chaotic mapping initialization and current optimum based search equations. Several UCI datasets have been used to evaluate the performance of EABC-SVM and the experimental results show that this approach has better classification accuracy and convergence performance than the ABC-SVM and other ABC variants-based SVM. Furthermore, the EABC-SVM can achieve a significant detection accuracy of 95% and reduce the amount of features about 65% in the conveyor belt fault detection.

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Section: Artificial Colony Bee (Abc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

Chen

Zhang

Wang

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In Reference [11], an algorithm combined wavelet packet decomposition with the canny operator was proposed, and this algorithm was also proved in laboratory experiments not only can ensure the validity and reliability of extracting line but also to reduce the calculation to improve the real-time performance of the system. An online visual belt inspection system was developed in Reference [12] used for detecting longitudinal rip and belt deviation from binary belt images. Reference [13] put forward a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network, in particular, a new weighted loss function was designed to optimize the network and to improve the detection effect on the conveyor belt edge.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision Based Conveyor Deviation Detection System

Zhang

Shi

et al. 2020

Applied Sciences

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The monitoring of conveyor belt deviation based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. Two kinds of conveyor condition monitoring algorithms are developed, applied and compared in this paper. Method 1 is to extract the edges of the conveyor belt based on the methods of edge detection and line fitting, and an analysis method is proposed to quickly determine whether the conveyor belt is off course. This method simplifies the information processing process after edge line extraction and realizes the fast and effective state detection of the conveyor belt. A conveyor belt deviation detection system was built in Method 2 with the aid of laser technology and the Labview platform. The result of deviation detection was more accurate and reliable with the addition of the laser line. It is observed that both methods can perform well for belt deviation detection, especially Method 2 that resulted in higher reliability and accuracy with faster processing speed. It provides new ideas for ensuring the safe and efficient operation of the conveyor.

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“…An increasing number of researchers have been paying attention to the detection of conveyor belt deviation, and it is a significant problem in coal mining [5][6][7][8]. Initial detection of conveyor belt deviation mainly relies on manual inspection, which is labor intensive and prone to errors and omissions.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network

Zeng

Zheng

2019

Algorithms

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The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.

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On-line conveyor belts inspection based on machine vision

Cited by 80 publications

References 6 publications

An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

An Enhanced Artificial Bee Colony-Based Support Vector Machine for Image-Based Fault Detection

A Computer Vision Based Conveyor Deviation Detection System

Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network

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