Comparative Analysis of deep learning based object detection model’s for their application in autonomous vehicles

Open Access

Year : 2022 | Volume : | Issue : 1 | Page : 2-6
By

    Umar Farooq

  1. Abdur Rehman

  2. Tabish Imtiaz

  3. M. Saad Alam

  1. Student, Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  2. Phd Scholar, Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

Abstract

In this work, we compare the detection accuracy and speed of several state of the art models for the task of detecting red and green traffic lights. We compare detection performance and speed of YOLOv4, ScaledYOLOv4 and YOLOR. All of these are single stage object detection models. Two stage models have good detection accuracy but are slower than single stage detectors, single stage detectors are faster and also have good detection accuracy which makes them reliable in real time object detection. We discuss about the object detection models and the evaluation metric that we used to score our models. Than we discuss about the results of our work.

Keywords: Object Detection, Self Driving Cars, Deep Learning, Traffic, Light Detection

[This article belongs to International Journal of Analog Integrated Circuits(ijaic)]

How to cite this article: Umar Farooq, Abdur Rehman, Tabish Imtiaz, M. Saad Alam Comparative Analysis of deep learning based object detection model’s for their application in autonomous vehicles ijaic 2022; 8:2-6
How to cite this URL: Umar Farooq, Abdur Rehman, Tabish Imtiaz, M. Saad Alam Comparative Analysis of deep learning based object detection model’s for their application in autonomous vehicles ijaic 2022 {cited 2022 Jul 07};8:2-6. Available from: https://journals.stmjournals.com/ijaic/article=2022/view=90461

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Regular Issue Open Access Article
Volume 8
Issue 1
Received June 16, 2022
Accepted June 23, 2022
Published July 7, 2022