Anchored versus Anchorless Detector for Car Detection in Aerial Imagery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.

Original languageEnglish
Title of host publication2021 2nd Global Conference for Advancement in Technology, GCAT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738132150
DOIs
Publication statusPublished - 01-10-2021
Event2nd Global Conference for Advancement in Technology, GCAT 2021 - Bangalore, India
Duration: 01-10-202103-10-2021

Publication series

Name2021 2nd Global Conference for Advancement in Technology, GCAT 2021

Conference

Conference2nd Global Conference for Advancement in Technology, GCAT 2021
Country/TerritoryIndia
CityBangalore
Period01-10-2103-10-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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