Computer Vision and Deep Learning-Enabled UAVs: Proposed Use Cases for Visually Impaired People in a Smart City

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1089)


Technological research and innovation have advanced at a rapid pace in recent years, and one group hoping to benefit from this, is visually impaired people (VIP). Technology may enable them to find new ways of travelling around smart cities, thus improving their quality of life (QoL). Currently, there are approximately 110 million VIP worldwide, and continuous research is crucial to find innovative solutions to their mobility problems. Recent advances such as the increase in Unmanned Aerial Vehicles (UAVs), smartphones and wearable devices, together with an ever-growing uptake of deep learning, computer vision, the Internet of Things (IoT), and virtual and augmented reality (VR)/(AR), have provided VIP with the hope of having an improved QoL. In particular, indoor and outdoor spaces could be improved with the use of such technologies to make them suitable for VIP. This paper examines use cases both indoors and outdoors and provides recommendations of how deep learning and computer vision-enabled UAVs could be employed in smart cities to improve the QoL for VIP in the coming years.


Deep learning Computer vision UAVs Drone Visually impaired people (VIP) Smart city 



Dr. Moustafa Nasralla would like to acknowledge the Department of Communications and Networks Engineering at Prince Sultan University (PSU) for the valued support and research environmental provision which have led to completing this work. Dr. Ikram Ur Rehman would like to thank the School of Computing and Engineering, University of West London for its support and research provision. Dr. Drishty Sobnath would like to thank the Research Innovation and Enterprise department at Solent University for its continuous support that led to the completion of this study. Dr. Sara Paiva would like to thank the Instituto Politécnico de Viana do Castelo for its continuous support that led to the completion of this study.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Communications and Networks EngineeringPrince Sultan UniversityRiyadhSaudi Arabia
  2. 2.School of Computing and EngineeringUniversity of West LondonLondonUK
  3. 3.Research, Innovation and EnterpriseSolent UniversitySouthamptonUK
  4. 4.ARC4DigiTInstituto Politécnico de Viana do CasteloViana do CasteloPortugal

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