EXPLORATION OF THE DETECTION METHOD OF THE CONDITION OF PATIENTS ON THE AMBULANCE STRETCHER

To improve the efficiency of ambulance emergency task scheduling and address the issues of low efficiency and poor real-time performance in traditional manual entry of stretcher status, this paper proposes an automatic judgment method for ambulance emergency status based on deep learning. A self-built ambulance stretcher dataset is constructed by combining 567 images captured independently from ambulances and 545 images obtained via web crawling. Data augmentation techniques are used to enhance model robustness, and mainstream object detection algorithms such as TOOD, Faster R-CNN, and YOLOv8 are compared and analyzed. Experimental results show that YOLOv8 achieves an average precision ([email protected]) of 0.779 at an IoU threshold of 0.5, with only 11.2 million parameters, significantly outperforming other models. This method can accurately and real-time determine the stretcher status, providing a reliable basis for emergency centers to dynamically dispatch ambulance resources. It effectively shortens the emergency response time and improves the success rate of emergency rescue.