Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

doi:10.3390/app8091575

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
	Tao Xian; Zhang Dapeng; Ma Wenzhi; Liu Xilong; Xu De; Xian Tao
发表期刊	APPLIED SCIENCES-BASEL
ISSN	2076-3417
	2018-09-01
卷号	8 期号:9 页码:15
通讯作者	Tao, Xian(taoxian2013@ia.ac.cn)
摘要	Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.
关键词	metallic surface autoencoder convolutional neural network defect detection
DOI	10.3390/app8091575
关键词[WOS]	FAULT-DIAGNOSIS ; INSPECTION ; CLASSIFICATION ; IMAGES ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61673383] ; National Natural Science Foundation of China[61503376] ; National Natural Science Foundation of China[61703399] ; Science Challenge Project[TZ2018006-0204-02] ; Science Challenge Project[TZ2018006-0204-02] ; National Natural Science Foundation of China[61703399] ; National Natural Science Foundation of China[61503376] ; National Natural Science Foundation of China[61673383]
项目资助者	Science Challenge Project ; National Natural Science Foundation of China
WOS研究方向	Chemistry ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000445760200164
出版者	MDPI
引用统计	被引频次：250[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21696
专题	中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Xian Tao
作者单位	Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Tao Xian,Zhang Dapeng,Ma Wenzhi,et al. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks[J]. APPLIED SCIENCES-BASEL,2018,8(9):15.
APA	Tao Xian,Zhang Dapeng,Ma Wenzhi,Liu Xilong,Xu De,&Xian Tao.(2018).Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks.APPLIED SCIENCES-BASEL,8(9),15.
MLA	Tao Xian,et al."Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks".APPLIED SCIENCES-BASEL 8.9(2018):15.

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