Surface defects on ceramic tiles not only affect their appearance,but may also shorten their service life and pose safety hazards to decoration.When using YOLOv8 deep model for tile surface defect detection,it is necessary to construct an effective training dataset to ensure the robustness of the model.A deep learning based data augmentation method for tile surface defect detection is proposed.Firstly,a high-resolution linear array camera is used to capture images of ceramic tiles,and combined with a public texture ceramic tile dataset,a ceramic tile dataset is constructed;Then,the Copy Paste algorithm is used to segment,transform,and paste the defect targets of the tile image into a new background image to improve the surface defect detection performance of the YOLOv8 depth model.The experimental results show that the method constructed and enhanced the tile dataset can effectively improve the tile surface defect detection ability of YOLOv8 depth model.
ceramic tile surface defect detectiondeep learningdata enhancementCopy-Paste algorithmYOLOv8 deep model