An improved YOLOv7-Tiny method for liquid level detection in medical infusion monitoring

Background: Intravenous infusion is a common medical intervention, but the need for constant monitoring of fluid levels increases the psychological burden on patients and the workload on healthcare providers. Intelligent infusion monitoring systems can address these issues by providing real-time alerts when the fluid level is low. However, existing methods struggle with accuracy and adaptability in detecting fluid levels under complex conditions. Methods: This study proposes an improved YOLOv7-tiny-based method for liquid level detection in medical infusion monitoring. To improve performance, three novel modules built upon the Extended Layers Aggregation Network (ELAN)—Dynamic Snake Convolution (DS)-ELAN, Deformable Convolution Network (DCN)-ELAN, and Partial Convolution (P)-ELAN—are proposed. These modules are designed to enhance detection accuracy for elongated structures, adapt to shape variations, and optimize computational efficiency for deployment on edge devices. The proposed method was trained on a dataset of 4296 annotated infusion bottle images captured under diverse lighting and environmental conditions. Performance was evaluated using metrics such as recall, mean average precision (mAP), and inference speed. Results: Experimental results demonstrate that the improved YOLOv7-tiny method achieves superior performance compared to the baseline YOLOv7-tiny, with a recall of 88.319% and mAP@[0.5:0.95] of 90.102%, while maintaining comparable computational complexity. Ablation studies confirm the independent contributions of each proposed module to the overall performance. The enhanced method also shows robust real-time capability on embedded devices. Conclusion: The proposed method significantly improves the accuracy and usability of intelligent infusion monitoring systems, enabling real-time detection of fluid levels in medical infusion bottles. This approach reduces the workload on healthcare providers, minimizes patient risks, and demonstrates potential for broader applications in medical monitoring scenarios.