In view of the problems of current tile defect detection mainly relying on manual detection， such as strong subjectivity， low efficiency， and high labor intensity， an improved lightweight algorithm for detecting small defects in large-format ceramic tile images based on YOLOv8 was proposed. Firstly， the high-resolution large-format image was cropped， and HorBlock was introduced into the backbone network to enhance model’s capture capability. Secondly， Large Separable Kernel Attention （LSKA） was incorporated to improve C2f for improving the detection performance of the model and model’s feature extraction capability was enhanced by introducing SA （Shuffle Attention）. Finally， Omni-Dimensional Dynamic Convolution （ODConv） was introduced to further enhance model’s capability to handle with small defects. Experimental results on Alibaba Tianchi tile defect detection dataset show that the improved model not only has lower parameters than the original YOLOv8n， but also has an increase of 8.2 percentage points in mAP@0.5 and an increase of 7 percentage points in F1 score compared to the original YOLOv8n. It can be seen that the improved model can identify and process small surface defects of large-format tiles more accurately， and improve the detection effect significantly while maintaining lightweight.