TY - GEN
T1 - Automatic License Plate Detection Using YOLOv9
AU - Nivethitha, V.
AU - Rajan, Shruthika
AU - Sriram, Suthir
AU - Thangavel, M.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - With the booming economy and better-Than-ever road infrastructure, a growth in the count of motor vehicles moving on the road has been noticed. This increase in the number of vehicles may lead to various problems happening such as increased frequency of accidents, more violations in motor violations, and sometimes may lead to crimes as well. Hence, vehicle monitoring becomes a huge factor in overcoming these situations since manual monitoring of vehicles will become obsolete due to the large volume of vehicles as well as the high speed at which they travel. In this study, a project has been evolved for license plate identification using a Convolutional Neural Network (CNN) which happens to be a technique used for the deep analysis of algorithms. [11] An automatic license plate detection system based on YOLOv9 has been presented as a means of advancing the state-of-The-Art in automatic license plate detection and contributing to the development of smarter, safer, and more sustainable urban environments.
AB - With the booming economy and better-Than-ever road infrastructure, a growth in the count of motor vehicles moving on the road has been noticed. This increase in the number of vehicles may lead to various problems happening such as increased frequency of accidents, more violations in motor violations, and sometimes may lead to crimes as well. Hence, vehicle monitoring becomes a huge factor in overcoming these situations since manual monitoring of vehicles will become obsolete due to the large volume of vehicles as well as the high speed at which they travel. In this study, a project has been evolved for license plate identification using a Convolutional Neural Network (CNN) which happens to be a technique used for the deep analysis of algorithms. [11] An automatic license plate detection system based on YOLOv9 has been presented as a means of advancing the state-of-The-Art in automatic license plate detection and contributing to the development of smarter, safer, and more sustainable urban environments.
KW - Bounding Box Method
KW - Computer Vision
KW - Image texture analysis
KW - License plate detection
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85216217583&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85216217583&partnerID=8YFLogxK
U2 - 10.1109/CYBERCOM63683.2024.10803199
DO - 10.1109/CYBERCOM63683.2024.10803199
M3 - Conference contribution
AN - SCOPUS:85216217583
T3 - 2024 International Conference on Cybernation and Computation, CYBERCOM 2024
SP - 236
EP - 240
BT - 2024 International Conference on Cybernation and Computation, CYBERCOM 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Cybernation and Computation, CYBERCOM 2024
Y2 - 15 November 2024 through 16 November 2024
ER -