Defect Detection of Metallized-Ceramic Rings Based on Fusion of Object Detection and Image Classification Networks

Aiming at the characteristics of small defect areas and less available information of metalized-ceramic rings, and the problem of low defect detection accuracy, a defect detection method of metalized-ceramic rings based on the fusion of target detection and image classification networks is proposed. First, an improved Faster- RCNN target detection network for small-area target detection is used to realize the preliminary identification and location of the defects. Then, the interpolation method is used to enlarge the located defect area, and the information association between the adjacent pixels of the image increases the feature information of the defect detection. Moreover, the ResNet image classification network is used to judge the defect category of the enlarged area. Finally, the final defect detection results were obtained using the target detection and image classification network results. The experimental results show that the proposed method can effectively improve the precision while ensuring defect detection recall and accurately locate the defect area.