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|Title:||Detection defect in printed circuit boards using unsupervised feature extraction upon transfer learning||Authors:||Volkau, Ihar
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Volkau, I., Abdul Mujeeb, Dai, W., Erdt, M., & Sourin, A. (2019). Detection defect in printed circuit boards using unsupervised feature extraction upon transfer learning. Proceedings of the 2019 International Conference on Cyberworlds (CW), 101-108. doi:10.1109/CW.2019.00025||Project:||SMA-RP4||Abstract:||Automatic optical inspection for manufacturing traditionally was based on computer vision. However, there are emerging attempts to do it using deep learning approach. Deep convolutional neural network allows to learn semantic image features which could be used for defect detection in products. In contrast to the existing approaches where supervised or semi-supervised training is done on thousands of images of defects, we investigate whether unsupervised deep learning model for defect detection could be trained with orders of magnitude smaller amount of representative defect-free samples (tenths rather than thousands). This research is motivated by the fact that collection of large amounts of defective samples is difficult and expensive. Our model undergoes only one-class training and aims to extract distinctive semantic features from the normal samples in an unsupervised manner. We propose a variant of transfer learning, that consists of combination of unsupervised learning used upon VGG16 with pre-trained on ImageNet weight coefficients. To demonstrate a defect detection, we used a set of Printed Circuit Boards (PCBs) with different types of defects - scratch, missing washer/extra hole, abrasion, broken PCB edge. The trained model allows us to make clusters of normal internal representations of features of PCB in high-dimensional feature space, and to localize defective patches in PCB image based on distance from normal clusters. Initial results show that more than 90% of defects were detected.||URI:||https://hdl.handle.net/10356/137974||DOI:||10.1109/CW.2019.00025||Rights:||© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2019.00025||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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