| Abstrakt: |
Aiming at the problems of minimal defect target, large difference of defect shape, easy missing detection and low accuracy in ceramic tile surface defects, an improved ceramic tile surface defect detection algorithm based on Faster RCNN is proposed. Firstly, based on the original Faster RCNN, resnet101 is selected as the feature extraction network, and deformable convolution networks is introduced in the last three stages of resnet101 to adaptively learn the defect features. Secondly, the regional proposals network is optimized, and the anchor generation parameters are improved through the analysis of ceramic tile data set, so that the generated anchors are more consistent with the target scale and the positioning is more accurate. Finally, the loss function is optimized and Rank & Sort Loss is introduced to reduce the number of super parameters and improve the performance of the model, making it more robust to the class imbalance problem in training. Experimental results show that the average detection accuracy of the improved Faster RCNN is 76.3%, which is 17.9 percentage points higher than that of Faster RCNN. It can better detect small target defects and meet the requirements of ceramic tile surface defect detection. [ABSTRACT FROM AUTHOR] |
