• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.317-319
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• 2021

In Korea, an intelligent transportation system(ITS) is established to efficiently operate traffic congestion on roads and is being used for traffic information collection and speed control systems. Currently, designated and dedicated lanes are in place to ensure traffic circulation and traffic safety, and systematic and accurate illegal vehicle crackdown systems with artificial intelligence technology are needed. In this study, we propose a vehicle number recognition model that can improve the efficiency of the traffic of designated vehicles. By applying computer vision technology, we are going to identify three-lane and four-lane multi-lane roads in real time and detect vehicle numbers by car to suggest ways to crack down on vehicles that violate the designated lane system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.63-72
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• 2014

With the rapid development in the economy and other fields as well, the standard of living in South Korea has been improved, and consequently, the demand of automobiles has quickly increased. It leads to various traffic issues such as traffic congestion, traffic accident, and parking problem. In particular, this illegal parking caused by the increase in the number of automobiles has been considered one of the main reasons to bring about traffic congestion as intensifying any dispute between neighbors in relation to a parking space, which has been also coming to the fore as a social issue. Therefore, this study looked into Daejeon Metropolitan City, the city that is understood to have the highest automobile sharing rate in South Korea but with relatively few cases of illegal parking crackdowns. In order to investigate the theoretical problems of the illegal parking, this study conducted a decision-making tree model-based Exhaustive CHAID analysis to figure out not only what makes drivers park illegally when they try to park vehicles but also those factors that would tempt the drivers into the illegal parking. The study, then, comes up with solutions to the problem. According to the analysis, in terms of the influential factors that encourage the drivers to park at some illegal areas, it was learned that these factors, the distance, a driver's experience of getting caught, the occupation and the use time in order, have an effect on the drivers' deciding to park illegally. After working on the prediction model, four nodes were finally extracted. Given the analysis result, as a solution to the illegal parking, it is necessary to establish public parking lots additionally and first secure the parking space for the vehicles used for living and working, and to activate the campaign for enhancing illegal parking crackdown and encouraging civic consciousness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.157-161
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• 2017

This paper introduce UHF miniaturized RFID tag antenna which is embedded on the seal-bolt or plastic bolt for automobile plate. To detect the illegal and un-registered car, the illegal automobile detection system has been developed using the seal-bolt UHF RIF tag antenna. The diameter of seal-bolt UHF tag is about 24mm, almost the same size as 100 Won coin. The simulated and measured reflection coefficient are compared, and the reading range patten is also measured. If seal-bolt tag is embedded on car plate, police can get information of automobile and detect illegal vehicles easily with the illegal automobile detection system.

• Annual Conference of KIPS
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• 2020.11a
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• pp.870-872
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• 2020

본 시스템은 어린이보호구역에 발생하는 차량사고가 불법주정차된 차량으로 인한 사각지대에 의해 발생되는 것에 착안하여 보행자를 인식하여 운전자들에게 알려 안전운전을 유도하여 사고를 예방해 주는 시스템이다 본 시스템은 영상인식장치, 경광장치, 중계장치, 차량 내 경고장치, 원격 트래픽 경고 수신기로 구성되어 있으며 영상인식장치가 edge-TPU 장치를 활용하여 카메라로부터 입력받은 영상을 모바일넷 기반의 딥러닝으로 처리하여 보행자, 차량, 그밖의 물체를 인식한다. 보행자가 인식되면 외부에서 경광장치가 발광하여 신호를 보내고, 중계장치를 통해 차량 내 경고장치로 보행자 경고 신호를 보낸다. 실험 결과 영상인식을 통해 보행자와 차량을 분류 인식할 수 있음을 확인하였다. 이러한 시스템은 어린이 보호구역에서 발생할 수 있는 교통사고를 방지하기 위해 효과적임을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.175-177
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• 2021

The parking area for the disabled is a parking facility for the pedestrian disabled and is a parking space for securing pedestrian safety passage for the disabled. However, due to the lack of social awareness of areas for the disabled, the use of parking areas is restricted, and violations such as illegal parking and obstruction of parking are increasing every year. Therefore, in this study, we propose a system to crack down on illegal parking in handicapped parking areas using the YOLOv5 model, a deep learning object recognition model to improve parking interference within parking spaces.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.69-75
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• 2022

In order to prevent traffic accidents for children who are vulnerable to traffic around elementary schools, a school zone in the children's protection zone is designated, and parking and stopping are prohibited in this area as the vehicle speed is less than 30km/h. However, Korea has a disgrace that the death rate of children from traffic accidents is the No. 1 among OECD countries. Against this backdrop, this study aims to contribute to preventing traffic accidents and raising awareness of driver safety by establishing an illegal parking and stopping system in the child protection zone due to various road conditions in the child protection zone. As a research method, a plan to build a parking and stopping prevention system was presented based on major preceding studies and literature investigation and analysis. Through the construction plan, effects such as preventing traffic accidents, inducing smart drivers to drive safely, strengthening pedestrian safety awareness, and inducing driver's awareness of safety can be expected.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.127-132
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• 2011

Load managing method on road became necessary since overloaded vehicles occur damage on road facilities and existing systems for preventing this damage still show many problems. Accordingly, efficient managing system for preventing overloaded vehicles could be organized by using the road itself as a scale by applying genetic algorithm to analyze the load and the drive information of vehicles. First of all, accurate analysis of load using the behavior of road itself is needed for solving illegal axle manipulation problems of overloaded vehicles and for installing intelligent embedded load analyzing system. Accordingly in this study, to use the behavior of road, the transformation was measured by installing underground box type indoor model and indoor experiment was held using genetic algorithm and 10% error were checked.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.146-151
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• 2021

Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.1-8
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• 2011

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.