Improving Surface Defect Detection for Quality Assessment of Car Body Panels

Surface quality analysis of exterior car body panels was still characterized by manual detection of local form deviations and subjective evaluation by experts. The approach presented in this paper is based on 3-D image processing. A major step towards automated quality control of produced panels is the classification of the different kinds of surface form deviations. In previous studies we compared the performance of different soft computing techniques for the detection of surface defect types. Although the dataset was rather small, high dimensional and unbalanced, we achieved promising results with regard to classification accuracies and interpretability of rule bases. In this paper we reconsider the collection of training examples and their assignment to defect types by the quality experts. For improving the reliability of the defect classification we try to minimize the uncertainty of the quality experts’ subjective and error-prone labelling. We build refined and more accurate classification models on the basis of a preprocessed training set that is more consistent. Improvements in classification accuracy using a partially supervised learning strategy were achieved.