The Application of Deep Learning and Image Processing Technology in Laser Positioning
In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be join...
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doaj-5f81443152454f419048c063d901d3ea2020-11-25T00:42:24ZengMDPI AGApplied Sciences2076-34172018-09-0189154210.3390/app8091542app8091542The Application of Deep Learning and Image Processing Technology in Laser PositioningChern-Sheng Lin0Yu-Chia Huang1Shih-Hua Chen2Yu-Liang Hsu3Yu-Chen Lin4Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanIn this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.http://www.mdpi.com/2076-3417/8/9/1542machine visionlaser spotdeep learningconvolutional neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chern-Sheng Lin Yu-Chia Huang Shih-Hua Chen Yu-Liang Hsu Yu-Chen Lin |
spellingShingle |
Chern-Sheng Lin Yu-Chia Huang Shih-Hua Chen Yu-Liang Hsu Yu-Chen Lin The Application of Deep Learning and Image Processing Technology in Laser Positioning Applied Sciences machine vision laser spot deep learning convolutional neural network |
author_facet |
Chern-Sheng Lin Yu-Chia Huang Shih-Hua Chen Yu-Liang Hsu Yu-Chen Lin |
author_sort |
Chern-Sheng Lin |
title |
The Application of Deep Learning and Image Processing Technology in Laser Positioning |
title_short |
The Application of Deep Learning and Image Processing Technology in Laser Positioning |
title_full |
The Application of Deep Learning and Image Processing Technology in Laser Positioning |
title_fullStr |
The Application of Deep Learning and Image Processing Technology in Laser Positioning |
title_full_unstemmed |
The Application of Deep Learning and Image Processing Technology in Laser Positioning |
title_sort |
application of deep learning and image processing technology in laser positioning |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2018-09-01 |
description |
In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image. |
topic |
machine vision laser spot deep learning convolutional neural network |
url |
http://www.mdpi.com/2076-3417/8/9/1542 |
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