Study and Evaluation of Quality Control Systems in Industrial Production Using Image Processing, Machine Learning and Deep Learning Techniques

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Cheraghi Shiva; Chalechale Abdolah

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Information Seminar Paper

Title

Study and Evaluation of Quality Control Systems in Industrial Production Using Image Processing, Machine Learning and Deep Learning Techniques

Author(s)

Cheraghi Shiva | Chalechale Abdolah

Keywords

Image Processing

Machine Learning

Deep Learning

Defect Detection

Web-Based Quality Control

Abstract

In recent years, with the growth of science and technology and the creation of competitive markets in various industries measuring the quantitative and qualitative parameters of the final products has become very important. Having a qualified product is the most important part of a production line. Nowadays, there are a few advanced factories that their production is not controlled by intelligent machine vision and Image Processing-based techniques usually on the Web. Real-time quality management allows increasing production efficiency. The main purpose of this article is to precisely analyze and evaluate some important and influential Image Processing-based, Machine Learning-based, and Deep Learning-based techniques used on the Web that have been employed in the field of quality control. The paper also aims to provide a proper road map for finding the most effective and efficient techniques for the future Web-based projects in the area of quality control.

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APA: Copy

Cheraghi, Shiva, & Chalechale, Abdolah. (). . . SID. https://sid.ir/paper/1046815/en

Vancouver: Copy

Cheraghi Shiva, Chalechale Abdolah. . . Available from: https://sid.ir/paper/1046815/en

IEEE: Copy

Shiva Cheraghi, and Abdolah Chalechale, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046815/en

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