AI-assisted pedestrian detection and enumeration from aerial perspectives

This paper presents a study on pedestrian detection and estimation from UAS imagery using recent YOLO-based object detection models. The objective is to evaluate model performance for identifying humans from aerial perspectives and to develop a customized detector suited for UAS applications. The study demonstrates the potential of combining modern artificial intelligence models with UAS-mounted vision systems for applications such as crowd monitoring, autonomous surveillance, and search-and-rescue operations. Experimental results demonstrated that the model achieved consistent detection accuracy up to 40m altitude, achieving near-perfect pedestrian identification with minimal false positives. The framework demonstrated its robustness for real-time deployment in aerial surveillance, search and rescue operations, and crowd monitoring scenarios.