TY - GEN
T1 - Traffic Congestion Detection Using Fixed-Wing Unmanned Aerial Vehicle (UAV) Video Streaming Based on Deep Learning
AU - Utomo, Winahyu
AU - Bhaskara, Putu Wisnu
AU - Kurniawan, Arief
AU - Juniastuti, Susi
AU - Yuniarno, Eko Mulyanto
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/17
Y1 - 2020/11/17
N2 - 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.
AB - 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.
KW - Congestion Detection
KW - UAV CNN
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85099642027&partnerID=8YFLogxK
U2 - 10.1109/CENIM51130.2020.9297921
DO - 10.1109/CENIM51130.2020.9297921
M3 - Conference contribution
AN - SCOPUS:85099642027
T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
SP - 234
EP - 238
BT - CENIM 2020 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Y2 - 17 November 2020 through 18 November 2020
ER -
