The work aims to automatically detect the defects on the medicinal glass bottles accurately and quickly. Based on the YOLOv3 algorithm, a learning detection model of deep convolutional neural network was built. The multi-scale feature information of glass bottles was extracted by connecting and normalizing the shallow and deep feature vectors of the neural network structure. To improve the accuracy of the model for the prediction of the bounding box, K-means clustering method was used to obtain the initial size of the anchor box. The multi-scale training strategy was used to enhance the robustness of the model to images of different sizes. The experimental results showed that, the proposed defect detection method of medical glass bottles could accurately detect and identify such defects as damaged tube end, gas lines, bubbles, scratches, stains and calculi on glass bottles. Compared with the mainstream target detection methods, the processing speed and accuracy of the proposed method were improved. The accuracy of defect target detection reached 96.23%, the recall rate was 93.82%, and the average accuracy was 89.35%. The proposed method has been successfully applied to several large medical glass packaging production companies in China, which has significantly improved the quality and qualified rate of medical glass packaging products and reduced the labor costs.