• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.59-64
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• 2024

Recently, research on the convergence of drones and artificial intelligence technologies have been conducted in various industrial fields. In this paper, we propose an illegal parking vehicle recognition model using deep learning-based object recognition and classification algorithms. The model of object recognition and classification consist of YOLOv8 and ResNet18, respectively. The proposed model was trained using image data collected in general road environment, and the trained model showed high accuracy in determining illegal parking. From simulation results, it was confirmed that the proposed model has generalization performance to identify illegal parking vehicles from various images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.127-128
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• 2017

해마다 대포차로 인한 사건 사고가 많이 일어나고 있으며, 피해율이 점점 더 증가하는 시점에서 검거율은 현저히 낮다. 이러한 문제를 줄이기 위해 대포차 검거 애플리케이션을 개발하고자 한다. 본 연구는 GPS와 사진으로부터 텍스트를 추출하는 기능을 활용하여 대포차를 검거하는 데 도움을 주는 애플리케이션이다. 사용자가 정차 및 주차되어 있는 차의 번호판을 사진 촬영 기능을 활용하여 자동으로 사진을 분석을 통해 차량의 번호를 인식하고, GPS를 활용하여 촬영한 장소의 위치 값을 추출하고 대포차 여부를 확인한다. 촬영한 차량이 대포차로 식별되면 관리 서버에 등록되고 대응 절차에 의해 대포차 검거 절차를 진행한다. 대포차의 실시간 검거를 위해 대포차 대응서버에서는 관리자에게 실시간으로 정보를 전송하고 알림 기능을 통해 검거 절차가 진행된다. 또한 실시간 대응에 어려움이 있는 상황에서는 자주 신고가 접수되는 출몰지역 정보를 관리자가 유추할 수 있도록 통계정보를 제공하여 추후 잠복에 의한 검거 정보를 제공한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.443-444
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• 2019

본 논문에서는 라즈베리파이(Raspberry Pi)와 실시간 객체 감지 기술인 YOLO를 이용한 전기차충전기앞불법주차 경고 영상인식 시스템을 제안한다. 최근 전기 자동차의 사용과 더불어 충전 인프라는 점점 늘어나는 중이지만, 여전히 전기차 충전기는 많지 않은 것이 현실이다. 전국 1,000여 곳이 넘는 전기차 충전소에 대해 법령으로 인한 규제를 시행 중임에도 불구하고 불법주차를 하는 일반차 오너들은 여전히 많다. 이로 인해 전기차 오너들은 충전에 많은 불편함이 있다. 이 시스템은 전기 자동차의 번호판을 인식하여 실시간 객체 감지 딥러닝 기법인 YOLO를 이용해 전기 자동차의 번호판에 특정 부분을 인식하고 특정 부분이 없는 일반 자동차가 전기차 충전기 앞 불법 주차를 하게 되면 부저와 LED경고를 통해 주차된 일반 차량에게 경고를 하여, 불법 주차자와 더불어 주변을 지나가는 행인들에게도 전기차 앞 불법 주차에 대해 각인을 시켜줄 수 있는 시스템이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.315-316
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• 2022

최근 전 세계적인 탄소 중립 정책으로 인해 전기차 보급 속도는 예상보다 훨씬 빠르게 증가하고 있다. 하지만 늘어나는 수요에 비해 전기차 충전기 수는 턱없이 부족하다. 그뿐만 아니라 일반 차들의 전기차 충전소 불법주차로 인해 전기차가 충전하지 못하는 불편함이 발생하고 있다. 본 논문에서는 에지 컴퓨터(edge computer)와 딥러닝 기반 객체 감지 시스템 YOLO(You only look once)를 이용한 전기차 충전소 불법주차 방지 시스템을 개발한다. 먼저, 이 시스템은 카메라를 통해 실시간으로 영상을 받아 YOLO를 이용하여 차량 번호판 인식이 되면 전기차 번호판의 특정 마크를 인식하여 전기차인지 일반 차인지를 판별하여 판별된 값에 따라 주차 차단기가 작동되는 시스템이다. 전기차이면 차단기가 내려가서 충전소를 이용할 수 있게 하고 일반차일 경우 주차 차단기가 내려가지 않고 막아 불법주차를 차단한다. 이와 같은 기술을 활용하여 전기차 충전소 불법주차 방지에 기여하고자 한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• Tribology and Lubricants
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• v.28 no.4
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• pp.184-188
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• 2012

Alcohol components are illegally mixed with petroleum products for tax evasion purposes, but this degrades vehicle performance. The alcohol content in petroleum fuel can be analyzed by gas chromatography. However, this technique requires a long analysis time and is expensive. $CrO_3$, also known as Jones reagent, changes color upon reaction with an alcohol. In this study, therefore, we analyze alcohol contents in vehicle fuel by using $CrO_3$ aqueous solution.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.661-680
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• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.13-20
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• 2016

In this study, we propose a method to regulate parking violations using computer vision technology. A still color image of the parked vehicle under question is obtained by a camera mounted on enforcement vehicles. The acquired image is preprocessed through a morphological algorithm and binarized. The vehicle's shadows are detected from the binarized image, and lanes are identified using the information from the yellow parking lines that are drawn on the load. Whether parking is illegal is determined by the conformity of the lanes and the vehicle's shadow.