Fault detection by a Gaussian neural network with reject options in glass bottle production

During the production of translucent glass bottles, many inspection procedures are realized in order to eliminate defects which produce dangerous consequences for customs. Checks on the neck of a bottle, which look like cracks in the glass, are one of the most important defects. Although an automated visual inspection system has been developed to solve this specific problem, its ability to cope with variations of the environment is limited and it requires careful tuning whenever the characteristics of the production change. In this paper, we propose a new approach based on computer vision and artificial neural network for check detection. The inspection procedure involves extracting features images of necks, the selection of the most discriminant features, and the decision is realized by a Gaussian neural network with reject options.