Abstract
This paper is the second part of a two-part article, which presents an adaptable automated visual inspection scheme (A2VIS) and its implementations. With the proposed A2VIS, the machine vision system can be deployed to undertake new inspection tasks and adapt to new operation conditions without excessive offline training efforts. In part I, the proposed A2VIS is described as a generic scheme at a conceptual level, with respect to the system components and structure, operating procedure, and performance measures. In part II, we focus on implementation details of A2VIS. For subject localization, an efficient edge-based template matching method is developed to allow tolerance against image distortion due to rotation and changes in size and position. Upon identifying the region of interest (ROI), image features are extracted through principal component analysis (PCA). Based on the extracted features, support vector machine (SVM) technique is employed to construct a reliable classification model. During model adaptation phase, online learning is used to build both the feature model and classification model for the purpose of performing inspection during the system training period. In the system evaluation with respect to the system efficiency and inspection accuracy, data collected from an automobile parts assembly inspection cell are used to assist system implementation and verify the fulfillment of design goals.
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Sun, J., Sun, Q. Further development of adaptable automated visual inspection—part II: implementation and evaluation. Int J Adv Manuf Technol 81, 1077–1096 (2015). https://doi.org/10.1007/s00170-015-7214-z
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DOI: https://doi.org/10.1007/s00170-015-7214-z