3D Object Detection and Localization within Healthcare Facilities

This study introduces a deep learning-based method for indoor 3D object detection and localization in healthcare facilities. This method incorporates spatial and channel attention mechanisms into the YOLOv5 architecture, ensuring a balance between accuracy and computational efficiency. The network achieves an AP50 of 67.6%, an mAP of 46.7%, and a real-time detection rate with an FPS of 67. Moreover, the study proposes a novel mechanism for estimating the 3D coordinates of detected objects and projecting them onto 3D maps, with an average error of 0.24 m and 0.28 m in the x and y directions, respectively. After being tested and validated with real-world data from a university campus, the proposed method shows promise for improving disinfection efficiency in healthcare facilities by enabling real-time object detection and localization for robot navigation.