Tear detection of conveyor belt based on machine vision

2021 ◽  
Author(s):  
Honglei Wang ◽  
Jiacheng Li ◽  
Taihui Wu ◽  
Xiaoming Liu ◽  
Junsheng Zhang
Keyword(s):  
Machine Vision ◽  
Conveyor Belt

Research of Mine Conveyor Belt Deviation Detection System Based on Machine Vision

2021 ◽  
Vol 57 (4) ◽  
pp. 703-712
Author(s):  
Taihua Wang ◽  
Zheng Dong ◽  
Jiaqi Liu
Keyword(s):  
Machine Vision ◽  
Detection System ◽  
Conveyor Belt

Online Analysis of Coal on A Conveyor Belt by use of Machine Vision and Kernel Methods

2010 ◽  
Vol 30 (6) ◽  
pp. 331-348 ◽  
Author(s):  
C. Aldrich ◽  
G. T. Jemwa ◽  
J. C. van Dyk ◽  
M. J. Keyser ◽  
J. H. P. van Heerden
Keyword(s):  
Machine Vision ◽  
Kernel Methods ◽  
Conveyor Belt ◽  
Online Analysis

Laser-based on-line machine vision detection for longitudinal rip of conveyor belt

Optik ◽  
2018 ◽  
Vol 168 ◽  
pp. 360-369 ◽  
Author(s):  
Li Xianguo ◽  
Shen Lifang ◽  
Ming Zixu ◽  
Zhang Can ◽  
Jiang Hangqi
Keyword(s):  
Machine Vision ◽  
Conveyor Belt ◽  
On Line ◽  
Vision Detection

scholarly journals Design and sorting of an object identification on machine vision by using line scan camera

2021 ◽  
Vol 3 (3) ◽  
pp. 100-112
Author(s):  
Zeravan M. Mosa ◽  
Erhan Akin
Keyword(s):  
Machine Vision ◽  
Moving Objects ◽  
Conveyor Belt ◽  
Object Identification ◽  
Line Scan ◽  
Geometric Patterns ◽  
Object Based ◽  
Physical Attributes ◽  
Pass Through ◽  
Good Object

This paper illustrates the design of a system to identify objects on a conveyor belt using machine vision. In the present study, a machine vision based on one line scan sorting was developed, the purpose being to sort objects based on various stages of maturity. Many different methods are available for object identification. But we made design a system that separates and counting them. Different objects placed on the conveyor belt moves along, a camera placed above the belt takes real-time video and feeds it to the MATLAB software for processing the object to compare with the basic template object. The vision camera understands an object based on its physical attributes, such as shape and size for effectively controlling the hardware, which will use in this work. Besides, the number of objects of a particular section that cross the conveyor to demonstrate the identification of moving objects is counted and displayed. A low-speed conveyor belt is manufactured with various test objects that pass through it. For identifying a good object, the wavelength data is used, determining the way to match the geometric patterns and to identify the dimensions, and edge detection is applied. The ability to count specific attributes objects is testing different test paths. The sorting of objects using machine vision was performed using an algorithm of pattern matching of machine vision. A pattern image template was built and stored in a computer's memory. When the object is sorting the application run, the camera receives the image of the object into MATLAB. The vision application investigates the image and transfers it to the classifier if the received image matches the model image or not matches.

Intelligent Bamboo Part Sorting System Design via Machine Vision

2021 ◽  
Vol 71 (1) ◽  
pp. 27-38
Author(s):  
Tian-Hu Liu ◽  
Zi-Di Wu ◽  
Qin-Ling Chen ◽  
Xiang-Ning Nie ◽  
Gui-Qi Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
High Speed ◽  
Learning Algorithm ◽  
Removal Rate ◽  
Rbf Neural Network ◽  
Conveyor Belt ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sorting System

Abstract The defect rate of initially produced block bamboo (Bambusoideae) parts is >20 percent. Sorting out these defective parts manually is a highly time-consuming and tedious process. An intelligent sorting system was developed based on machine vision using a Radial Basis Function (RBF) neural network learning algorithm in this study. First, a high-speed charge-coupled device camera was used to obtain a series of images of perfect and defective block bamboo parts. Next, the RBF neural-network learning algorithm was applied to obtain defect characteristics and to locate defective parts moving forward on a conveyor belt. An array of air jets was designed to force defective parts off the belt. Experimental results showed that the average defective part removal rate of the proposed system was 91.7 percent.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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