To address the problem of low detection accuracy in realtime detection of defects on the surface of flat ceramic films, this paper proposes a YOLOv5 ceramic film defect detection method that incorporates coordinate attention and adaptive features. By adding a coordinate attention mechanism to the backbone network of the original YOLOv5 model, the relationship between location information and channels is established to obtain the region of interest more accurately. The adaptive feature fusion mechanism is incorporated into the prediction network of the original network to improve the detection capability of the model for multi-scale defects. Replace the spatial pyramid pooling module in the original network with the spatial pyramid pooling module of the null space convolution pooling module to improve the convolutional kernel field of view to obtain more useful information. The experimental results show that the average accuracy of this model is 97.8%, the number of detection frames is 32 FPS, and the average accuracy is improved by 5.5% compared with the original YOLOv5 model. The model proposed in this paper improves the detection accuracy of the model under the condition of satisfying the real-time detection of flat ceramic film defects, which has certain reference value for promoting the development of flat ceramic film defect detection.