Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137973
Title: Towards automatic optical inspection of soldering defects
Authors: Dai, Wenting
Abdul Mujeeb
Erdt, Marius
Sourin, Alexei
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Dai, W., Abdul Mujeeb, Erdt, M., & Sourin, A. (2018). Towards automatic optical inspection of soldering defects. Proceedings of the 2018 International Conference on Cyberworlds (CW), 375-382. doi:10.1109/CW.2018.00074
Project: SMA-RP4
Abstract: This paper proposes a method for automatic image-based classification of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Machine learning-based approaches are frequently used for image-based inspection. However, a main challenge is to manually create sufficiently large labeled training databases to allow for high accuracy of defect detection. Creating such large training databases is time-consuming, expensive, and often unfeasible in industrial production settings. In order to address this problem, an active learning framework is proposed which starts with only a small labeled subset of training data. The labeled dataset is then enlarged step-by-step by combining K-means clustering with active user input to provide representative samples for the training of an SVM classifier. Evaluations on two databases with insufficient and shifting solder joints samples have shown that the proposed method achieved high accuracy while requiring only minimal user input. The results also demonstrated that the proposed method outperforms random and representative sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the uncertainty sampling method by ~ 0.5%.
URI: https://hdl.handle.net/10356/137973
DOI: 10.1109/CW.2018.00074
Rights: © 2018 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.2018.00074
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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