Experimental investigation of human and machine-vision arrangements in inspection tasks

The traditional job of a quality control inspector, whether human or a machine-vision device, is to discriminate products into acceptable and non-acceptable items. The performance of inspectors can be measured in terms of rejecting non-acceptable items (Type I error) or classifying non-acceptable items as acceptable (Type II error). Reinspection is often used afterward to remedy this situation if required. Unfortunately, all such inspections remain prone to errors. This paper uses an experimental investigation to research the various ways in which the positive attributes of human and machine-vision inspection systems can be arranged to achieve enhanced system performance in industrial inspection tasks.