Pairing human and machine-vision in industrial inspection tasks

The traditional job of a quality control inspector, whether a human or a machine- vision device, is to discriminate products into "acceptable" and "rejectable" items. Acceptable items are those which confirm to a set of predetermined standards or quality characteristics. The performance of inspectors can be measured in terms of rejecting conforming items (type I error) or classifying non-confirming items as acceptable (type II error). Such inspection systems tend to be error prone. Reinspection is often used as a final means to remedy this situation and ensure higher outgoing quality. Effectiveness is limited by the fault rate effect, which shows decreasing defect detection performance with successive stages of reinspection. This paper uses a model of the inspector as a conservative signal detector to examine the various ways in which the positive attributes of human and machine-vision inspection systems can be combined to achieve enhanced system performance.