Comparison of image processing algorithms and neural networks in machine vision inspection

This system use PC as the core of image analysis and processing, with the single chip processor as the control core execution, combining with machine vision image processing technology, using advanced image processing algorithms, to achieve separation of the nut, and through experiments to test the correctness of the algorithm. The system has the advantage of a fast processing speed and high reliability. It not only save the manpower cost, but also improve the efficiency of the nut sorting.

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Automatic Welding Track Detection for Welding Process by Machine Vision

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.462.886 ◽

2012 ◽

Vol 462 ◽

pp. 886-890

Author(s):

G. Kiswanto ◽

A. Rianto

Keyword(s):

Image Processing ◽

Machine Vision ◽

Direct Contact ◽

Welding Process ◽

Line Detection ◽

Automatic Welding ◽

Median Filtering ◽

Hough Transforms ◽

Image Processing Algorithms ◽

Processing Algorithms

The use of machine vision technology in the welding process has been developed along with the need for more consistent and more qualified welding results. The application of machine vision to perform object analysis without direct contact to the object itself making the whole processes fast while maintaining the accuracy. In this study, some image processing algorithms are combined which include : image enhacement (grayscaling and median filtering), edge detection, binarization, and Hough Transform for line detection. The candidate of welding tracks are produced through this standard algorithm. However, afterward, some modifications to the Hough transforms are carried to enable finding the actual welding tracks. The implemented method is capable of detecting the actual welding tracks and their appropriate welding path with accuracy not more than 0.9 mm.

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Double-stream Convolutional Neural Networks for Machine Vision Inspection of Natural Products

Applied Artificial Intelligence ◽

10.1080/08839514.2018.1428491 ◽

2017 ◽

Vol 31 (7-8) ◽

pp. 643-659 ◽

Cited By ~ 1

Author(s):

Przemysław Dolata ◽

Mariusz Mrzygłód ◽

Jacek Reiner

Keyword(s):

Neural Networks ◽

Natural Products ◽

Machine Vision ◽

Convolutional Neural Networks ◽

Vision Inspection ◽

Machine Vision Inspection

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Reconfigurable Image Acquisition in the Machine Vision Inspection System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.197.376 ◽

2012 ◽

Vol 197 ◽

pp. 376-380

Author(s):

Da Xing Zhao ◽

Lei Peng ◽

Guo Dong Sun ◽

Wei Feng

Keyword(s):

Image Processing ◽

Machine Vision ◽

Image Matching ◽

Visual Inspection ◽

Detection System ◽

Image Acquisition ◽

Inspection System ◽

Image Detection ◽

Vision Inspection ◽

Machine Vision Inspection

Since camera drivers provided by the different manufacturers are not compatible, machine vision systems must be redeveloped according to specific camera. It is great significant to work out the problem, which could improve the versatility of the inspection system. The reconfigurable technology has applied to image processing, image matching and so on. Hence, in the paper the reconfigurable image acquisition module is designed, which reserves some interfaces for the image detection module. Citing the nonel visual inspection system as an example, adopting DALSA and BASLER cameras to acquire the images, the images was displayed properly. Therefore, the compatibility of the image detection system has been improved greatly.

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