Low-cost alternative solutions have advanced last-mile delivery, with Unmanned Aerial Vehicles (UAVs) emerging as a promising option for logistics tasks. However, as UAV operations increasingly occur in densely crowded urban areas, safety concerns - especially for people nearby - have intensified. To ensure safe deliveries, real-time UAV path planning is essential for avoiding no-fly zones defined around individuals detected along the route. This study addresses this challenge by evaluating human detection confidence in UAV imagery using the YOLOv7 model and a custom dataset. It also estimates individuals’ positions through the Monoplotting technique. The customized YOLOv7 model achieved an average precision of 53.8% and an inference time of 9.9 ms, supporting real-time deployment. Human detection confidence exceeded 85% for individuals located approximately 30 meters from the UAV when flying at altitudes up to 30 meters. However, detection accuracy declined with greater distances or higher altitudes. The Monoplotting method produced an average planimetric error of 4.296 meters, with errors increasing as the distance between the UAV and the detected person increased, mainly due to the UAV’s inertial system. These findings offer valuable insights for enhancing the safety and reliability of UAV-based last-mile delivery operations. Continued advancements in human detection are essential to support the scalable and responsible integration of UAVs into urban logistics systems.