Real-Time Object Detection System for Hospital Assets Using YOLOv8

Hospital administration is essential in the provision of high-quality social services to patients. Hospitals must have efficient asset management to offer quality medical care. On the other hand, many hospitals face problems such as data entry errors. Based on this problem, the author hopes to solve it by implementing real-time object detection and recording data distribution using the YOLO (You Only Look Once) algorithm. This data distribution will then be applied to the current system. Performance tests were carried out in this research using the YOLO architecture, especially on YOLOv8. one of the improvements of popular deep learning algorithms. This research used 7680 images (augmentation) which were divided into 3 parts. 6720 training data (88%), 640 validation data (8%), and 320 (4%) test data. 7680 data were added from 16 tested medical device categories with 200 images per category. This research has an average accuracy of 90%, an average precision of 94%, and an average recall value of 92.2%. These results show that YOLOv8 performs well in detecting medical devices. To improve accuracy, it is recommended to test larger and more diverse datasets. This research helps the healthcare industry better monitor and manage real-time assets.