UAV Infrared Image Human and Vehicle Object Detection Based on YOLOv5 - Zhao, Leihao (International Journal of Computer Science and Information Technology, 2024)
Currently, the primary methods for searching for missing persons and vehicles in the wilderness involve extensive manpower for ground-based grid searches. Occasionally, a limited number of helicopters are deployed to assist in the search and rescue operations, and in some cases, UAVs equipped with visible light imaging cameras are used for manual image recognition. However, these search methods are inefficient and require significant human and material resources. To address these issues, this paper proposes a search and rescue method that combines multi-rotor UAVs with image target recognition technology. The YOLOv5-based object detection method is employed to identify humans and vehicles in UAV infrared images, achieving a mean Average Precision (mAPs) of 99%, with a mAP of 98.5% for human recognition and 99.5% for vehicle recognition. This object recognition algorithm model has profound implications for the automation of multi-rotor UAV wilderness search and rescue operations.