A defect detection method for glass bottle mouth based on area segmentation

Quality inspection is an essential technology in the glass product industry. Machine vision has shown more significant potential than manual inspection at present. However, the visual inspection of the bottle for defects remains a challenging task in a quality-controlled due to the difficulty in detecting screw thread defects. To overcome the problem, we propose a bottle mouth detection method based on area segmentation. First, an area segment method with traditional image processing methods, which is based on the characteristics of screw thread, was proposed. According to the result of the segment, the bottle area is divided into a screw thread area and a non-screw thread area. For the former, a defect detection method that uses edge detection and Gaussian filters is proposed to precisely detect screw thread defects. For the latter, a defect detection method that uses techniques such as the Sobel algorithm and global binarization is proposed to precisely detect other defects. The proposed method is tested for data sets obtained by our designed vision system. The experimental result demonstrates that our framework achieves good performance.