Research progress for UAV search and rescue methods based on deep learning

With the development of artificial intelligence technology, Unmanned Aerial Vehicles (UAVs) can automatically identify and locate objects of interest by equipping different optoelectronic payloads, and are used for Search and Rescue (SaR) missions in disaster environments. UAV search and rescue missions require the rapid location of missing persons in complex and dangerous environments, facing challenges such as single detection technique, susceptibility to interference during imaging, complex background, and limited UAV platform resources. In recent years, the integration of UAV systems with deep learning algorithms has emerged as a prominent research focus. To demonstrate the advancement of UAV search and rescue techniques, the review of these methodologies has been conducted. Firstly, the difficulties faced by UAV search and rescue are analyzed according to the characteristics of UAV search and rescue missions. Subsequently, various UAV search and rescue methods based on deep learning in recent years are sorted out. Additionally, object detection datasets relevant to UAV search and rescue missions have been sorted out according to different data types. Finally, the problems of current research and future development directions are summarized.