Knowledge Commons of Institute of Automation,CAS
Combining Prior Knowledge With CNN for Weak Scratch Inspection of Optical Components | |
Hou, Wei1,2; Tao, Xian1,2; Xu, De1,2 | |
发表期刊 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
ISSN | 0018-9456 |
2021 | |
卷号 | 70期号:1页码:11 |
摘要 | Scratches as the major defects in precision optical components are caused inevitably in the manufacturing process, which is harmful to the whole optical system. Most scratches on the surface of optical components are weak scratches with low contrast and uneven distribution of gray scale, which poses a significant problem for inspection. In this article, an end-to-end weak scratch inspection method based on novel scratch-enhancement methods and convolutional neural network (CNN) is proposed for optical components. To enhance weak scratches, a local maximum index (LMI) module and a direction-sensitive convolution (DSC) module are proposed to generate multilevel-feature maps using prior knowledge about scratch. Different from previous works utilizing the raw dark-field image as network input, these multilevel features are used as the inputs of encoder-decoder module for training. After training, the whole inspection model can infer weak scratches from raw dark-field test images in an end-to-end manner. Experimental results show that the proposed model achieves pixel accuracy of 92.48% and IoU at 77.27% on the test data set. It outperforms the networks without adding prior knowledge, which shows that prior knowledge is much helpful for weak scratch inspection. Moreover, compared with other classical methods and CNN-based methods, the proposed method achieves the best performance in the weak scratch inspection. |
关键词 | Convolutional neural network (CNN) direction-sensitive convolution (DSC) local maximum index (LMI) optical component weak scratch inspection |
DOI | 10.1109/TIM.2020.3011299 |
关键词[WOS] | DEFECT DETECTION ; CLASSIFICATION ; SURFACES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61703399] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000594910700047 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42774 |
专题 | 中国科学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 | Tao, Xian |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China |
第一作者单位 | 精密感知与控制研究中心 |
通讯作者单位 | 精密感知与控制研究中心 |
推荐引用方式 GB/T 7714 | Hou, Wei,Tao, Xian,Xu, De. Combining Prior Knowledge With CNN for Weak Scratch Inspection of Optical Components[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70(1):11. |
APA | Hou, Wei,Tao, Xian,&Xu, De.(2021).Combining Prior Knowledge With CNN for Weak Scratch Inspection of Optical Components.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70(1),11. |
MLA | Hou, Wei,et al."Combining Prior Knowledge With CNN for Weak Scratch Inspection of Optical Components".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70.1(2021):11. |
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