Pipeline tracking and event classification for an automatic inspection vision system

Author(s):  
Felipe R. Petraglia ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia
2012 ◽  
Vol 522 ◽  
pp. 628-633 ◽  
Author(s):  
Jian Zhe Chen ◽  
Gui Tang Wang ◽  
Jian Qiang Chen ◽  
Xin Liang Yin

Small plastic gear is generally made by injection molding.But the injection molding process and mold have problems with missing tooth, shrink, more material, less material and inaccurate roundness and so on. Furthermore, using manual inspection will appear phenomenon of low efficiency, false detection and leak detection. To solve these problems, this paper introduces an automatic inspection system of small plastic gears based on machine vision. The system consists of feeding and sorting machine control system and machine vision inspection system of the gear defects. Mechanical control use digital servo control technology to achieve automatic nesting, feeding, positioning of gear workpiece, and depend on the inspection result of machine vision system to sort. After acquired gear image through a camera, Machine vision system uses median filtering, binarization, edge detection algorithms to process image. Then the system adopts template matching algorithm to obtain the inspection result and send the result to the sorting controller, which achieve automatic smart inspection of gear. The automatic inspection system has accurate, efficient, intelligent and other advantages.


Author(s):  
Yakov Frayman ◽  
◽  
Hong Zheng ◽  
Saeid Nahavandi ◽  

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.


2017 ◽  
Vol 23 (5) ◽  
pp. 339-346
Author(s):  
JongSeop Park ◽  
Sang-yub Ji ◽  
Hoyeon Kim ◽  
Jae-Soo Cho

2004 ◽  
Vol 37 (4) ◽  
pp. 301-307 ◽  
Author(s):  
H.I Shafeek ◽  
E.S Gadelmawla ◽  
A.A Abdel-Shafy ◽  
I.M Elewa

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