A Compact Convolutional Neural Network for Surface Defect Inspection

doi:10.3390/s20071974

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 智能机器人团队

	A Compact Convolutional Neural Network for Surface Defect Inspection
	Huang, Yibin 1,3; Qiu, Congying 2; Wang, Xiaonan 3; Wang, Shijun 3; Yuan, Kui 3
刊名	SENSORS
	2020-04-01
卷号	20 期号:7 页码:19
关键词	surface defect inspection convolutional neural network machine vision
DOI	10.3390/s20071974
通讯作者	Wang, Xiaonan(wangxiaonan2012@ia.ac.cn)
英文摘要	The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units). to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units). Our model consists of a light-weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3-4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating-point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspection (ASI).
资助项目	National Key R&D Program of China[2018YFB1306500] ; National Natural Science Foundation of China[61421004]
WOS关键词	FEATURE-EXTRACTION
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	MDPI
WOS记录号	WOS:000537110500170
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/39640]
专题	自动化研究所_智能制造技术与系统研究中心_智能机器人团队
通讯作者	Wang, Xiaonan
作者单位	1.95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Columbia Univ, Civil Engn & Engn Mech Dept, New York, NY 10024 USA 3.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Yibin,Qiu, Congying,Wang, Xiaonan,et al. A Compact Convolutional Neural Network for Surface Defect Inspection[J]. SENSORS,2020,20(7):19.
APA	Huang, Yibin,Qiu, Congying,Wang, Xiaonan,Wang, Shijun,&Yuan, Kui.(2020).A Compact Convolutional Neural Network for Surface Defect Inspection.SENSORS,20(7),19.
MLA	Huang, Yibin,et al."A Compact Convolutional Neural Network for Surface Defect Inspection".SENSORS 20.7(2020):19.