SDM-YOLO11n: a lightweight and high-precision infusion monitoring method.

Current infusion monitoring methods primarily rely on two technological approaches: non-visual sensor technology and visual sensor technology, for real-time monitoring of the remaining liquid volume in infusion bottles within infusion scenarios. However, non-visual sensor-based methods often suffer from complex installation procedures and are prone to external interference, while visual sensor-based methods tend to exhibit low detection accuracy in complex infusion environments involving small targets, low contrast, tilted objects, and partial occlusions, making it difficult to accurately monitor the remaining liquid. To address these challenges, we propose a high-precision and lightweight object detection algorithm-SDM-YOLO11n-based on an improved version of YOLO11n. Specifically, a lightweight Split-Parallel Convolution (SPConv) module is introduced to enhance the backbone network's spatial modeling capabilities and improve feature extraction efficiency; the traditional upsampling operation is replaced with a Dynamic Sampling (DySample) module for more adaptive feature reconstruction and multi-scale information fusion; and a Mixed Local Channel Attention (MLCA) mechanism is incorporated to strengthen attention to key regions of infusion bottles and their internal liquids, thereby further improving detection accuracy. In addition, a method based on the ratio of geometric parameters of oriented bounding boxes is proposed to precisely estimate the remaining liquid volume in infusion bottles. Experimental results show that SDM-YOLO11n improves mAP@0.5:0.95 by 0.6 percentage points compared to YOLO11n, with a model size of only 5.1 MB. The proposed algorithm achieves high-precision detection of infusion bottles and their internal liquids in complex scenarios and enables real-time monitoring of the remaining liquid volume in multiple infusion bottles.