Abstract

--- - "Highlights What are the main findings? The paper presents a dataset with two sub-sets of urban traffic images captured by a drone at two different heights (100 and 200 m) and with two different road configurations (intersection and roundabout). The paper studies different data partition techniques that lead to significantly different prediction performances, some of which might be deceivingly better while not reflecting the likely performance of a model in practice. The paper presents benchmark results for popular detection methods (YOLOv11, YOLOv12, YOLOv11-OBB, RT-DETR) focusing on studying data selection methods for more realistic model performance assessment, and highlighting how these methods need much improvement before it can be said that they can generalize well, e.g., for even slightly different camera views. What are the implications of the main finding? Compared to other UAV datasets, this dataset contains a significant number of motorbikes (a primary means of private transport in developing countries). This will challenge researchers elsewhere on the detection of small objects (motorbikes in this case) and to study robustness of methods to the domain shift of different camera views. When data comes from video sequences, the selection of data for building deep-learning object detection/localization models needs to be done carefully. Traditional random sampling generates data leakage (testing data \"contaminated\" with similar training data) that results in over-estimation of model performance.Highlights What are the main findings? The paper presents a dataset with two sub-sets of urban traffic images captured by a drone at two different heights (100 and 200 m) and with two different road configurations (intersection and roundabout). The paper studies different data partition techniques that lead to significantly different prediction performances, some of which might be deceivingly better while not reflecting the likely performance of a model in practice. The paper presents benchmark results for popular detection methods (YOLOv11, YOLOv12, YOLOv11-OBB, RT-DETR) focusing on studying data selection methods for more realistic model performance assessment, and highlighting how these methods need much improvement before it can be said that they can generalize well, e.g., for even slightly different camera views. What are the implications of the main finding? Compared to other UAV datasets, this dataset contains a significant number of motorbikes (a primary means of private transport in developing countries). This will challenge researchers elsewhere on the detection of small objects (motorbikes in this case) and to study robustness of methods to the domain shift of different camera views. When data comes from video sequences, the selection of data for building deep-learning object detection/localization models needs to be done carefully. Traditional random sampling generates data leakage (testing data \"contaminated\" with similar training data) that results in over-estimation of model performance.Abstract 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 Medell & iacute;n, 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."