Dataset: Roundabout Aerial Images for Vehicle Detection

Enrique Puertas, Gonzalo De-Las-Heras, Javier Fernández-Andrés and Javier Sánchez-Soriano
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Enrique Puertas: Department of Science, Computing and Technology, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
Gonzalo De-Las-Heras: SICE Canada Inc., Toronto, ON M4P 1G8, Canada
Javier Fernández-Andrés: Department of Engineering, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
Javier Sánchez-Soriano: Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain

Data, 2022, vol. 7, issue 4, 1-11

Abstract: This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Keywords: roundabouts; aerial; dataset; UAV; object detection; machine learning; ADAS; PASCAL VOC; autonomous driving; deep learning; neural networks; RetinaNet (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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