基于机器视觉和深度学习的材料缺陷检测应用综述

doi:10.11896/cldb.20070136

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Abstract The safety problem caused by material defects has always been a hot issue, and how to realize the rapid accurate identification and location of material defects is the focus of current research on material defects. The traditional non-destructive testing method mainly uses ultrasonic, X-ray and other advanced technologies to realize the identification and positioning of material defects. Although this method solves the problem of low manual testing efficiency, it is still difficult to achieve the requirements of intelligentization, automation and high precision.
Advances in the computer technology has stimulated the rapid development of machine vision in the detection of material defects. The advantages of machine vision inspection technology are mainly the combination of non-destructive inspection, automation and intelligentization, which has not only good safety and high efficiency, but also high detection accuracy. However, an image processing algorithm usually can be effective to the recognition of only one specific kind of defect, which causes low versatility of the equipment and elevated cost of production and maintenance.
In recent years, the rise of deep learning has promoted the rapid development of the field of artificial intelligence, and solved the problem that traditional machine vision needs different image processing algorithms to classify different tasks. Deep learning has also become a popular research direction in material defect detection. Many scholars have applied deep learning technology to the field of material defect detection, and they have achieved good results whether they are related to the classification of material defects, or the location and the segmentation of material defects.
The article summarizes the application of traditional methods and machine vision methods in material defects, introduces the principles of deep learning in material defect detection, and systematically describes the application of deep learning in the classification, positioning and segmentation of material defects at home and abroad, and the future development trend of the application of deep learning in the field of material defect detection is prospected.

Key words： machine vision material defect detection deep learning convolutional neural network

Published: 25 August 2022 Online: 2022-08-29

CLC number:

TP391.4

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	YANG Chuanli
	ZHANG Xiuqing

Cite this article:

YANG Chuanli,ZHANG Xiuqing. Survey of Applications of Material Defect Detection Based on Machine Vision and Deep Learning[J]. Materials Reports, 2022, 36(16): 20070136-9.

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http://www.mater-rep.com/EN/10.11896/cldb.20070136 OR http://www.mater-rep.com/EN/Y2022/V36/I16/20070136

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