Automated Machine Vision System for Inspecting Cutting Quality of Cubic Zirconia

In this paper, we present an automated system for the visual inspection of cubic zirconia (CZ) cut quality. In particular, we inspect the cut quality from pavilion facets of the CZ. For the hardware, the system includes a computerized-control mechanical part that performs both the task of feeding the CZ to the inspection station and the task of separating the gemstone according to the inspection result. In terms of software, we propose an image processing algorithm that consists of two major steps. For the first step, pavilion facets are extracted from the CZ image acquired from the pavilion side. In particular, we resort to the idea of 1-D edge detection in conjunction with random sample consensus line fitting for the pavilion facet extraction. For the second step, a set of measures derived from the extracted facet structure are calculated and are used for cut quality judgment as either accept or reject. The metrological analysis of the system is also investigated. We perform an experiment to inspect 1756 object images consisting of both good and bad samples. The performance of our system yields to about 5.21% of false reject rate and 0% of false acceptance rate. The system can inspect CZ with a rate of 1 sample/s.