PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects

doi:10.3390/machines9100221

	PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects
	Lei, Linjian1,2,3,4 ; Sun, Shengli 1,4; Zhang, Yue 1,2,4; Liu, Huikai 1,2,4; Xu, Wenjun 1,4
	2021-10
发表期刊	MACHINES (IF:2.1[JCR-2023],2.2[5-Year])
EISSN	2075-1702
卷号	9 期号:10
DOI	10.3390/machines9100221
摘要	Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net.
关键词	surface defect detection pixel-wise segmentation image-wise classification convolutional neural network deep learning
URL	查看原文
收录类别	SCIE
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic ; Engineering, Mechanical
WOS记录号	WOS:000711523000001
出版者	MDPI
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/128595
专题	信息科学与技术学院_博士生
通讯作者	Sun, Shengli
作者单位	1.Chinese Acad Sci, Shanghai Inst Tech Phys, Shanghai 200083, Peoples R China; 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China; 3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China; 4.Chinese Acad Sci, Key Lab Intelligent Infrared Percept, Shanghai 200083, Peoples R China
第一作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Lei, Linjian,Sun, Shengli,Zhang, Yue,et al. PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects[J]. MACHINES,2021,9(10).
APA	Lei, Linjian,Sun, Shengli,Zhang, Yue,Liu, Huikai,&Xu, Wenjun.(2021).PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects.MACHINES,9(10).
MLA	Lei, Linjian,et al."PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects".MACHINES 9.10(2021).