The work aims to proposea detection method for glass vial appearance defects based on an improved YOLOv11 to address the low detection accuracy and high miss detection rate for small defect targets in pharmaceutical glass vial detection. Firstly, a dynamic snake convolution network was introduced into the backbone network of YOLOv11. By adaptively focusing on different defect characteristics, it effectively concentrated on defect features of various shapes and sizes, enhancing the model's ability to extract local structural characteristics of defects. Secondly, a multi-scale dilated attention mechanism was constructed in the shallow network to comprehensively capture and integrate multi-scale feature information. Finally, a small target detection layer was added to capture rich detailed information from the shallow features of the network structure, further improving the detection capability for small defect targets. Experimental results demonstrated that the improved YOLOv11 method achieved a mean average precision of 88.38% on the prefilled syringe dataset, representing a 3.8% improvement over the baseline model, with particularly outstanding performance in small target detection. The proposed method effectively enhances the detection accuracy of pharmaceutical glass vial detects, providing a practical solution for the field of automated inspection.