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2021 | OriginalPaper | Chapter

Intelligent Control Location Detection System Based on Machine Vision and Deep Learning

Authors : Jin Yao, Jing Feng, Yuzhou Liu, Licheng Chen, Rentang You, Jiaxing Sun, Xiaofei Zhang, Yongzhi Xiang, Xiaoyun Chen, Jiajie Wu

Published in: Big Data Analytics for Cyber-Physical System in Smart City

Publisher: Springer Singapore

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Abstract

Nowadays, the competition in the manufacturing industry is more and more fierce, in order to save costs, improve production efficiency and automation degree, there is an urgent need for enterprises to improve the production line. In this process, the application of machine vision technology is more and more extensive, especially in the industrial production of location detection, development is particularly rapid. The purpose of this paper is to provide technical Suggestions for the optimization of the intelligent control positioning detection system based on machine vision and deep learning. This article is to major scientific research project “mechatronics” the researches on the mechanism of the low voltage circuit breaker intelligent test as the backing, in the research background of low-voltage circuit breaker production testing, using distributed control technology, testing technology and intelligent control technology, machine vision technology, sensor technology and combining with field production experience, research and development of intelligent control orientation detection system based on machine vision technology, the results showed that the rate of each link adaptation remain below the 112 ms. The system improves the existing manual testing method, which can quickly, stably and accurately detect the location, so as to adapt to the increasingly severe competition.

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Metadata
Title
Intelligent Control Location Detection System Based on Machine Vision and Deep Learning
Authors
Jin Yao
Jing Feng
Yuzhou Liu
Licheng Chen
Rentang You
Jiaxing Sun
Xiaofei Zhang
Yongzhi Xiang
Xiaoyun Chen
Jiajie Wu
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-4572-0_145

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