Traffic Congestion Detection Using Fixed-Wing Unmanned Aerial Vehicle (UAV) Video Streaming Based on Deep Learning

Winahyu Utomo, Putu Wisnu Bhaskara, Arief Kurniawan, Susi Juniastuti, Eko Mulyanto Yuniarno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Population growth in the region has led to increased use of roads that causes traffic congestion. Traffic congestion also occurs during long weekend in outsides city. The roads in the area are usually smooth traffic flow, it becomes very congested. One method of the smart road monitoring system is a fixed camera sensor as input and artificial intelligent as the analysis. However, these methods require a great infrastructure on highways such as: power supply, protective CPU, good power supply and stable computer network connections. This will be difficult to fulfill if applied on roads outside the city. To overcome the problem, we propose a system of vehicles detection and road density classification using Fixed-Wing Unmanned Aerial Vehicle (UAV) video streaming. We chose Fixed-Wing UAV for its advantages: wide range and fast flight speed. The proposed system detects and classifies vehicles. Vehicles are detected and calcified using CNN's Deep Learning which uses the YOLO architecture. The level of traffic density is determined by the area of the road covered by vehicles to road area. We tested the proposed system using YouTube video and UAV video streaming. Both experimental scenarios have almost the same results. Precisions of recording video UAV and streaming video are: 90.75% and 90%, respectively.

Original languageEnglish
Title of host publicationCENIM 2020 - Proceeding
Subtitle of host publicationInternational Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-238
Number of pages5
ISBN (Electronic)9781728182834
DOIs
Publication statusPublished - 17 Nov 2020
Event2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia
Duration: 17 Nov 202018 Nov 2020

Publication series

NameCENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period17/11/2018/11/20

Keywords

  • Congestion Detection
  • UAV CNN
  • YOLO

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