Fractional Derivatives for Edge Detection: Application to Road Obstacles

  • Roy Abi Zeid DaouEmail author
  • Fabio El Samarani
  • Charles Yaacoub
  • Xavier Moreau
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Detecting road obstacles is a major challenge in autonomous vehicles for smart transportation and safe cities. They have always been a major problem, as they increase car accidents leading to mechanical/electrical problems, injuries, and even deaths. Several methods exist to detect these road abnormalities, allowing for an earlier control of the car dynamics. While these methods rely on different sensors and multiple viewpoints, the one applied in this work is based on edge detection within a single view acquired from a conventional digital camera. In addition, as the fractional calculus has shown good results in several engineering domains, this mathematical technique is deployed for the already developed edge detection techniques. Hence, the novelty in this chapter consists of detecting road abnormalities using fractional calculus techniques. Results show very good identification of road obstacles, especially concerning humps, bumps, and cushions. In addition, fractional methods show better results compared to conventional methods in terms of response time and edge sharpness.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roy Abi Zeid Daou
    • 1
    • 2
    Email author
  • Fabio El Samarani
    • 3
    • 4
  • Charles Yaacoub
    • 3
  • Xavier Moreau
    • 4
  1. 1.Faculty of Public Health, Biomedical Technologies DepartmentLebanese German UniversityJouniehLebanon
  2. 2.MART Learning, Education and Research CenterChnaniirLebanon
  3. 3.Faculty of EngineeringHoly Spirit University of KaslikJouniehLebanon
  4. 4.IMS Laboratory, Group CRONEBordeaux UniversityBordeauxFrance

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