Real-time object detection by cameras mounted on mobile robots on campus - Xu, Haozhe (Nanyang Technological University, 2025)
This dissertation aims to identify and apply campus popularization objectives and strengthen campus management and security. Because most of the detection is for vehicles and pedestrians which often requires high accuracy and real-time performance. This dissertation uses the YOLOv5 algorithm, which realizes real-time object detection. After big data training, the feasibility and accuracy of the algorithm are guaranteed, and the most effective model is determined. Finally, the camera is mounted on the robot to realize dynamic detection. In addition, some feasible solutions are also put forward to solve some problems encountered in the detection process.