The surface of aluminum foil in drug blister packaging contains various information on fonts and patterns, and the surface of aluminum foil is uneven, leading to the uneven distribution of light and dark. To address the problem that the YOLOX model cannot more accurately distinguish the defect features from the surface features of aluminum foil, a surface defect detection method based on the improved YOLOX model was proposed. Firstly, in order to enhance the globality of the information input to the Prediction, it was necessary to analyze the global features of the feature map in the Neck network, so the CSP module of the Neck network was replaced with the transformer encoder module. At the same time, the YOLOX model has a deep depth, and to effectively improve the classification accuracy, the Mish activation function was utilized to replace the Swish activation function. Then, focal loss was introduced into the loss function to solve the problem of difficulty in classifying defect regions and background regions due to the similarity of defect features and aluminum foil surface features. The experimental results show that the mAP of the improved model for the detection of aluminum foil surface defects was 90.17%, which was 4.95% higher than the original YOLOX model, and that the improved model can reduce the probability of false detection and missed detection of defects with high similarity to the surface features of aluminum foil.