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Comparative Analysis of Intelligent Driving and Safety Assistance Systems Using YOLO and SSD Model of Deep Learning

Author

Listed:
  • Nidhi Sindhwani

    (Amity School of Engineering and Technology, Amity University, Delhi, India)

  • Shekhar Verma

    (Amity School of Engineering and Technology, Amity University, Delhi, India)

  • Tushar Bajaj

    (Amity School of Engineering and Technology, Amity University, Delhi, India)

  • Rohit Anand

    (G.B. Pant Engineering College, Delhi, India)

Abstract

Bad road conditions are one of the main causes of road accidents around the world. These kinds of accidents prove to be fatal as many lives are lost in these accidents that are mainly caused by potholes or distress on surface of roads. This paper suggests a system that will not only help in reducing the chances of these accidents by making the driver aware of the upcoming distress/potholes on the road but also saving the location of these potholes which can be sent to respective authorities so that they can be repaired. The authors have used technologies like image processing, computer vision, deep learning, and internet of things (IoT) to make this happen. It uses a camera mounted in front near windshield that will capture the images which will be further be processed to get the location of the potholes and distress on road. These detected potholes can be projected on a heads-up display (HUD) placed near windshield which will notify the driver of the potholes.

Suggested Citation

  • Nidhi Sindhwani & Shekhar Verma & Tushar Bajaj & Rohit Anand, 2021. "Comparative Analysis of Intelligent Driving and Safety Assistance Systems Using YOLO and SSD Model of Deep Learning," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(1), pages 131-146, January.
    • Handle: RePEc:igg:jismd0:v:12:y:2021:i:1:p:131-146
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