期刊
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
卷 8, 期 4, 页码 689-698出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCPMT.2018.2794540
关键词
Convolutional neural network (CNN); defect recognition; machine vision; multitask learning; spring-wire sockets
类别
资金
- National Natural Science Foundation of China [61703399, 61673383, 61733004, 61421004, 61403382]
As a critical electrical connector component in the modern industrial environment, spring-wire sockets and their manufacture quality are closely relevant to equipment safety. These types of defects in a component are difficult to properly distinguish due to the defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To satisfy the requirements of quality control, a machine vision apparatus for component inspection is presented in this paper. With a brief description of the apparatus system design, our emphasis is put on the defect recognition algorithm. A multitask convolutional neural network (CNN) is proposed for detecting those ambiguous defects. Compared with the image processing method in machine vision, the defect inspection problem is converted into object detection and classification problems. Instead of breaking it down into two separate tasks, we jointly handle both aspects in a single CNN. In addition, data augmentation methods are discussed to analyze their effects on defects recognition. Successful inspection results using the presented model are obtained using challenging real-world defect image data gathered from a spring-wire socket module inspection line in an industrial plant.
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