Translator Disclaimer
3 October 1995 Optimizing the performance of near-perfect defect detection machines
Author Affiliations +
Developers of machine vision systems for industrial applications are frequently exposed to the problem of proving to their customers that specified performance measures are met. A typical example would be the rate of correct classification in defect detection machines that usually will be in the range of 95 - 100%. We call such machines near perfect. In practice this figure is stated for the complete inspection decision, which in general is based on a number of subdecisions made by the machine. An example would be surface inspection in industrial production, where a workpiece will be rejected if one or several defects are detected. Let's assume that the probability of false classification for a single defect is p1. In the case where several defects may appear on the surface every defect contributes to the final decision, with the probability of a wrong decision p2. It would appear logical that p2 is larger than p1, because the more defects are found on the surface, the more likely the system would make a wrong decision (all the p1s for the single defects would add up). In this paper we show that although seeming paradox the reverse is true. We show that with estimates of p1, the joint decision can be optimized such that the actual error rate of the defect detection machine is less than p1. We also give practical recommendations on how to tune the pattern recognizers to achieve optimal performance.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wolfgang Poelzleitner "Optimizing the performance of near-perfect defect detection machines", Proc. SPIE 2597, Machine Vision Applications, Architectures, and Systems Integration IV, (3 October 1995);


Back to Top