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Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management

Author

Listed:
  • Sergio Bemposta Rosende

    (Department of Science, Computing and Technology, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Sergio Ghisler

    (Stirling Square Capital Partners LLP, London SW3 4LY, UK)

  • Javier Fernández-Andrés

    (Department of Industrial and Aerospace Engineering, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Javier Sánchez-Soriano

    (Higher Polytechnic School, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain)

Abstract

A dataset of Spanish road traffic images taken from unmanned aerial vehicles (UAV) is presented with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which involves the acquisition of the data and images, the labeling of the vehicles, anonymization, data validation by training a simple neural network model, and the description of the structure and contents of the dataset (which amounts to 15,070 images). The images were captured by drones (but would be similar to those that could be obtained by fixed cameras) in the field of intelligent vehicle management. The presented dataset is available and accessible to improve the performance of road traffic vision and management systems since there is a lack of resources in this specific domain.

Suggested Citation

  • Sergio Bemposta Rosende & Sergio Ghisler & Javier Fernández-Andrés & Javier Sánchez-Soriano, 2022. "Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management," Data, MDPI, vol. 7(5), pages 1-10, April.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:5:p:53-:d:801439
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    References listed on IDEAS

    as
    1. Sergio Bemposta Rosende & Javier Sánchez-Soriano & Carlos Quiterio Gómez Muñoz & Javier Fernández Andrés, 2020. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants," Energies, MDPI, vol. 13(21), pages 1-23, November.
    2. Merkert, Rico & Bushell, James, 2020. "Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control," Journal of Air Transport Management, Elsevier, vol. 89(C).
    3. Melih Yildiz & Burcu Bilgiç & Utku Kale & Dániel Rohács, 2021. "Experimental Investigation of Communication Performance of Drones Used for Autonomous Car Track Tests," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
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