Automated Inspection of Aluminum Castings using Classifier Fusion Strategies

Generally, the flaw detection in automated visual inspection consists of two steps: a) identification of potential defects using image processing techniques, and b) classification of potential defects into ‘defects’ and ‘regular structures’ (false alarms) using a pattern recognition methodology. In the second step, since several features can be extracted from the potential defects, a feature selection must be performed. In this paper, several known classifiers are studied in the automated visual inspection: threshold, Euclidean, Mahalanobis, polynomial, support vector machine (SVM) and neural network. First, the performance of the classifiers is assessed individually. Second, the classifiers are combined in order to improve their performance. Seven fusion strategies in the combination were evaluated: ‘and’, ‘or’, ‘majority vote’, ‘product’, ‘sum’, ‘max’ and ‘median’.