Infusion Stand and Hospital Bed Position Matching Dataset

The current medical industry faces challenges in equipment management and patient safety monitoring, especially during infusions, where the relative position of the infusion stand and hospital bed is crucial. However, existing monitoring systems often lack precise target detection capabilities, making it difficult to effectively identify and track equipment positions. This dataset aims to provide high-quality image data to help researchers develop more accurate target detection algorithms to improve the safety and management efficiency of medical equipment. The dataset contains 5000 precisely annotated images, taken in high resolution. Data collection was conducted using professional medical equipment in real medical environments, including wards and operating rooms. We implemented multiple rounds of annotation and consistency checks to ensure annotation accuracy. Data is stored in JPG format, with a clear structure, facilitating subsequent processing and analysis. The core advantages of this dataset are high annotation accuracy and consistency, with an annotation error rate below 2%. Additionally, we have employed new data augmentation techniques to enhance model generalization capabilities. Using this dataset, the performance of target detection algorithms has improved significantly, with the F1 score increasing by 15%.