Aerial Person Detection for Search and Rescue: Survey and Benchmarks

Robust person detection in aerial images under all-weather conditions stands as a fundamental technology pivotal to the efficacy of intelligent search and rescue (SaR) tasks. However, the challenges stem from the varied postures, sparsity, diminutiveness, and faintness of personnel objects when viewed from an air-to-ground perspective, leading to issues with insufficient feature representation and suboptimal detection accuracy. This survey commences by underscoring the extensive potential applications and the prevailing limitations associated with aerial person detection (APD) within the scope of drone-assisted SaR scenarios. To meet the requirement of APD applications, we thoroughly investigate advancements and challenges in 4 related methodologies, including object-aware methods for size and perspective variability, sample-oriented methods with sparse distribution, information-fusion methods for the issue of lighting or visibility, and lightweight methods on constrained devices. Furthermore, to foster advancements in APD, we have conducted a comprehensive APD dataset labeled as “VTSaR”, which stands out from the existing publicly accessible APD datasets by offering a greater diversity of scenes, varying personnel behaviors, flexible capture angles, differing capture heights, and an inclusion of aligned visible and infrared samples along with synthetic samples. Finally, we evaluate the performance of mainstream detection methods on VTSaR benchmarks, advocating for APD’s broader application across various domains.