Defect Detection and Classification Method of Ceramic Tiles Based on Improved YOLOv8n

At present, most surface defect of ceramic tiles are still detected by workers with low production efficiency and low detection accuracy. In order to solve this problem, we proposed a ceramic tile defect detection and classification method based on YOLOv8n, with dynamic serpentine convolution and attention mechanism. Firstly, C2F-DSConv was used to replace the original C2F in the backbone network and neck network of the yolov8 model to enhance the extraction of scratch defect features. Secondly, the CBAM attention module was added to the model to enable it to learn useful features from the image more efficiently, so as to improve the model performance. Finally, the loss function CIoU was modified to the Inner-SIoU loss function to enhance the model’s feature extraction ability for small targets. It is experimentally shown that, as compared with that of the original model, the average detection accuracy of the improved yolov8n-based tiles defect detection model is increased by 2.6%, while the number of the computation is only increased by 0.5 G. In addition, the proposed method showed obvious advantages in comparative experiments and self-built data set experiments.