• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.189-193
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• 2024

According to the Korean Road Traffic Act, continuous driving in the overtaking lane (1 lane) of the highway is judged as a violation of the designated lane. Currently, in order to crack down on the advanced situation on the highway, a citizen's report or the road police directly determine whether it is a violation and crack down. This is because a violation is judged by overtaking or not the speed of the vehicle on the highway, and it is difficult to judge whether the vehicle is continuously driving because the standard is ambiguous in CCTV. Therefore, a system that self-determines and regulates whether the first lane is continuously driving without human intervention is needed. In this paper, in order to enable multiple object tracking during object tracking and to ensure the system's real-time feasibility, an unmanned crackdown system was designed based on F-SORT (Focused-Simple Online and Realtime Tracking) based on the Simple Online and Realtime Tracking (SORT) model, and the system determines whether or not the vehicle is continuously driving in one lane by determining the moving distance of the vehicle

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.126-142
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• 2023

The purpose of this study is to develop unmanned traffic enforcement equipment and standard specifications for the prevention of traffic accidents and violations of the two-wheeled vehicle laws. To this end, we conducted a review of the problems and new technologies of the currently operating unmanned traffic enforcement equipment on two-wheeled vehicles. And through a survey, the feasibility of introducing unmanned traffic enforcement equipment for two-wheeled vehicles and the current status of technology were investigated. In addition, the two-wheeled vehicle enforcement function was implemented through field tests of the development equipment, and the addition of enforcement targets and the number recognition rate were improved through performance improvement. Based on the results of field experiments and performance evaluation, performance standards for unmanned two-wheeled vehicle traffic enforcement equipment were prepared, and in the communication protocol, two-wheeled vehicle-related matters were newly composed in the vehicle classification code and violation items to develop standards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.465-468
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• 2022

The incidence of traffic accidents is increasing every year, and Korea is among the top OECD countries. In order to improve this, various road traffic laws are being implemented, and various traffic control methods using equipment such as unmanned speed cameras and traffic control cameras are being applied. However, as drivers avoid crackdowns by detecting the location of traffic control cameras in advance through navigation, a mobile crackdown system that can be cracked down is needed, and research is needed to increase the recognition rate of vehicle license plates on the side of the road for accurate crackdown. This paper proposes a method to improve the vehicle number recognition rate on the road side by applying a gradient correction algorithm using image processing. In addition, custom data learning was conducted using a CNN-based YOLO algorithm to improve character recognition accuracy. It is expected that the algorithm can be used for mobile traffic control cameras without restrictions on the installation location.