Defect Detection of Texture Tile Using Improved YOLOv3

The present tile defect detection algorithms mainly rely on manual design features and classifier. In addition, they face debugging difficulties and insufficient robustness in practical applications. Therefore, we proposed a texture tile defect detection algorithm using the improved YOLOv3 model. First, a convolutional autoencoder was added in front of the Darknet-53; the reconstructed images with weak defects were fused with original images to get richer input information. Further, the K-means clustering method was used to get new and more suitable anchors. Finally, to solve the problem of insufficient samples, we used the weights of a pre-trained model trained on a common data set to initialize the network to improve convergence performance. Results show that the average accuracy of the improved model increased by 5 percent, besides it kept the prediction speed of the original model and could effectively detect texture tile holes and scratches.