Detection of Surface Defects of Magnetic Tiles Based on Improved YOLOv5

Abstract

The typical defect detection algorithm is ineffective due to the contrast between the magnetic tile defect and the various defect features. An improved YOLOv5‐based algorithm, for detecting magnetic tile defects with varying defect features, is suggested. The procedure begins by incorporating the CBAM into feature extraction network of YOLOv5. It improves the feature of network learning capabilities for the target region by filtering and weighting the feature vectors in such a way that the processing of network is dominated by the essential target characteristics. A new loss function of detection model is then proposed according to the properties of the magnetic tile picture, and the confidence of prediction box is increased. Data augmentation technologies are introduced to increase the number of data samples. Based on magnetic tile defect datasets, the evaluation results have shown that the precision of the proposed approach is 98.56%, 3.21%, and 7.22% greater than the original YOLOv5 and Faster R‐CNN, respectively, all of which demonstrate the effectiveness and accuracy of the proposed method.