The existing detection method of ceramic tile surface defects has the problem of insufficient ability to identify small target defects, and the detection speed needs to be improved. Therefore, this study proposes a ceramic tile surface defect detection method based on improved YOLOv5. Firstly, due to the small size of ceramic tile surface defects, the detection abilities of three target detection head branches of YOLOv5s are compared and analyzed. It is found that the effectiveness of the model that removes the large target detection head and retains only the medium and small target detection heads is optimal. Secondly, to further realize the lightweight of the model, the study applies ghost convolution and C3Ghost modules to replace the ordinary convolution and C3 modules of YOLOv5s in the Backbone network, thus reducing the number of model parameters and the calculation amount. Finally, the coordinate attention mechanism module is added at the end of the Backbone and Neck networks of YOLOv5s to solve the problem of no attention preference in the original model. The proposed method is tested on the Tianchi ceramic tile defect detection dataset. The results show that the mean precision of the improved detection model averages 66%, which is 1.8% higher than the original YOLOv5s model. Besides, the size of the model is only 10.14 MB, and the number of parameters and the calculation amount is reduced by 48.7% and 38.7% respectively compared with the original model.