Further development of adaptable automated visual inspection—part I: concept and scheme

Abstract

The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely A2VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of A2VIS in a systemic perspective. In part I, the proposed A2VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the A2VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of A2VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM).