Neural Networks Inspection System for Glass Bottles Production

This paper describes a vision system that detects cracks in glass bottles production. The first step consists in collecting prototypes of bottles with and without defects. A sequence of 16 images is captured by a matrix camera while each bottle rotates in front of a specific lighting system. The second step is concerned with morphometric and photometric features extraction. The subsequent decision step is performed by different neural networks, such as MLP, RBF, PNN and LVQ. Finally, performances of these networks have been compared. All the images of bottles without defects have been recognized but a few images with small cracks, which are very important defects, have not been identified. However, since each bottle is represented by a sequence of 16 images, cracks will appear in at least three or four images, so that a defective bottle can be detected at least one time through the sequence. Therefore the decision system recognizes good and defective bottles with a very high rate of success.