This paper presents an intelligent visual inspection methodology that addresses the need for an improved adaptability of a visual inspection system for parts verification in assembly lines. The proposed system is able to adapt to changing inspection tasks and environmental conditions through an efficient online learning process without excessive off-line retraining or retuning. The system consists of three major modules: region localization, defect detection, and online learning. An edge-based geometric pattern-matching technique is used to locate the region of verification that contains the subject of inspection within the acquired image. Principal component analysis technique is employed to implement the online learning and defect detection modules. Case studies using field data from a fasteners assembly line are conducted to validate the proposed methodology.
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Sun, J., Sun, Q., Surgenor, B. (2009). An Adaptive Machine Vision System for Parts Assembly Inspection. In: Ao, SI., Rieger, B., Chen, SS. (eds) Advances in Computational Algorithms and Data Analysis. Lecture Notes in Electrical Engineering, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8919-0_14
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DOI: https://doi.org/10.1007/978-1-4020-8919-0_14
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