Deep learning for intelligent transportation: A method to detect traffic violation

Smart transportation is being envisaged as an important parameter in building smart cities. Although conceptualized to have major advantages, lack of intelligent systems makes more vulnerable for disasters. The number of fatality due to road accident has increased up to 12% in 2022 as that of previo...

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Bibliographic Details
Main Authors: Rajagopal, Manikandan, Sivasakthivel, Ramkumar
Format: Conference Proceeding
Language:English
Subjects:
Cities
Computer vision
Deep learning
Machine learning
Object recognition
Smart cities
Traffic accidents
Traffic violations
Transportation
Urban areas
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Summary:Smart transportation is being envisaged as an important parameter in building smart cities. Although conceptualized to have major advantages, lack of intelligent systems makes more vulnerable for disasters. The number of fatality due to road accident has increased up to 12% in 2022 as that of previous year says the WHO report. There are large number of new vehicles plying on roads which makes space constraint for the commuters. This makes a large number of traffic violations happening in urban areas. The smart cities insist and tries to adopt AI based methods for identifying traffic violations. Computer Vision are predominant solution in detecting traffic violation. This paper proposes a Deep learning method using famous YOLOV technique for object detection for effectively determining the traffic violation. The violations such as signal cross are concentrated in this research. The experimental results prove that the proposed technique has 95.1% of classification accuracy in detecting signal crosses.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0158376