UTUAV: A Drone Dataset for Urban Traffic Analysis
Felipe Lepin, Sergio A. Velastín, Roberto León, Jesús García-Herrero, Gonzalo Rojas-Martínez, Jorge E. Espinosa-Oviedo · Drones · 2025
Vehicle detection from unmanned aerial vehicles (UAVs) has gained increasing attention due to the growing availability and accessibility of these platforms. UAV-captured videos have proven valuable in a variety of applications, including agriculture, security, and search and rescue operations. To support research in UAV-based vehicle detection, this paper introduces UTUAV: Urban Traffic Unmanned Aerial Vehicle, a dataset composed of traffic video images collected over the streets of Medellín, Colombia. The images are recorded from a semi-static position at two different altitudes (100 and 120 m) and include three manually annotated vehicle types: cars, motorcycles, and large vehicles. The analysis focuses on the main characteristics and challenges presented in the dataset. In particular, data leakage occurs when a single video is used to construct the training, validation, and evaluation sets. An inadequate data split can result in highly similar samples leaking into the evaluation set, leading to inflated performance metrics that do not reflect a model’s true generalization ability. Additionally, baseline results from recent state-of-the-art object detection models based on CNNs and Transformers (YOLOv8, YOLOv11, YOLOv12 and RT-DETR) are presented. The experiments highlight several challenges, including the difficulty of detecting small-scale objects, especially motorcycles, and limited generalization capabilities under altitude changes, a phenomenon commonly referred to as domain shift.