• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.379-387
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• 2001

We proposed an enhanced extraction method of vehicle plate, in which both the brightness variation of gray and the Hue value of HSI color model were used. For the extraction of the vehicle plate from a vehicle image, first of all, candidate regions for the vehicle plate were extracted from the image by using the property of brightness variation of the image. A real place region was determined among candidate regions by the density of pixels with the Hue value of green and white. For- extracting the feature area containing characters from the extracted vehicle plate, we used the histogram-based approach of individual characters. And we proposed and applied for the recognition of characters the enhanced ART2 algorithm which support the dynamical establishment of the vigilance threshold with the genera]iced union operator of Yager. In addition, we propose an enhanced SOSL algorithm which is integrated both enhanced ART2 and supervised learning methods. The performance evaluation was performed using 100's real vehicle images and the evaluation results demonstrated that the extraction rates of tole proposed extraction method were improved, compared with that of previous methods based un brightness variation, RGB and HSI individually . Furthermore, the recognition rates of the proposed algorithms were improved much more than that of the conventional ART2 and BP algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.138-141
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• 2014

본 논문에서는 색상 정보를 이용하여 배경 영역이 포함된 자동차의 전,후면 사진에서의 자동차 번호판 영역(녹색, 흰색) 추출과 추출된 번호판에서 글자를 분리해내는 방법을 제안한다. 기존의 색상 정보를 이용하여 번호판을 추출하는 방법은 흰색 번호판(신형 번호판)의 경우에는 배경 영역에서 흰색인 영역도 많고 국내 차량 중에 흰색 차량이 많기 때문에 번호판 영역과 배경 영역 사이의 명확한 구분에 어려움이 있었다. 따라서 행별 Red값 변화도를 조사하여 배경 영역과 번호판 영역 사이의 명확한 구분을 하게 하며, 흰색 번호판의 경우에 추출이 안되면 흰색의 기준을 더 낮추어서 다시 영역 추출을 할 수 있는 재추출 알고리즘을 추가해서 비교적 어두운 사진에서도 번호판영역을 추출할 수 있도록 한다. 추출된 번호판에서 글자를 추출해내는 과정에서도 이진화를 거치면 노이즈가 많이 생기기 때문에 이를 줄이고자 행별 Red값 변화도를 조사하여 번호판 영역에서 위아래 부분의 노이즈를 줄일 수 있도록 하였다.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.06a
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• pp.111-112
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• 2002

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.40-48
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• 2018

This paper proposes a license-plate recognition algorithm for automobile black-box image which is obtained from the camera moving with the automobile. The algorithm intends to increase the overall recognition-rate of the license-plate by increasing the Korean character recognition-rate using multi-stage neural network for automobile black-box image where there are many movements of the camera and variations of light intensity. The proposed algorithm separately recognizes the vowel and consonant of Korean characters of automobile license-plate. First, the first-stage neural network recognizes the vowels, and the recognized vowels are classified as vertical-vowels('ㅏ','ㅓ') and horizontal-vowels('ㅗ','ㅜ'). Then the consonant is classified by the second-stage neural networks for each vowel group. The simulation for automobile license-plate recognition is performed for the image obtained by a real black-box system, and the simulation results show the proposed algorithm provides the higher recognition-rate than the existing algorithms using a neural network.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.313-319
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• 2007

In this paper, we propose a novel method to extract an area of car licence plate and codes of vehicle number from a photographed car image using features on vertical edges and a new Fuzzy neural network algorithm to recognize extracted codes. Prewitt mask is used in searching for vertical edges for detection of an area of vehicle number plate and feature information of vehicle number palate is used to eliminate image noises and extract the plate area and individual codes of vehicle number. Finally, for recognition of extracted codes, we use the proposed Fuzzy neural network algorithm, in which FCM is used as the learning structure between input and middle layers and Max_Min neural network is used as the learning structure within inhibition and output layers. Through a variety of experiments using real 150 images of vehicle, we showed that the proposed method is more efficient than others.

• Annual Conference of KIPS
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• 2017.11a
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• pp.785-788
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• 2017

우리나라의 차량 범죄율은 국민들의 소득 증가와 더불어 계속해서 증가 중인 추세이다. 현재 상용화 된 차량 번호판 인식은 시스템은 특정 우치에 고정되어 있고, 화면의 특정 영역에 물체가 들어와야만 번호판을 인식할 수 있다. 단순히 그 영역에 들어오지 못하면 번호판을 인식하지 못하고 지나치게 된다. 본 연구는 특정 영역에 구애받지 않고 장소, 화면 어디에서든 차량 번호판을 인식할 수 있게 딥 러닝 기술을 응용하여 범죄차량을 찾아내는 기법을 제안한다. 또한 서버와 연동시켜 실시간으로 범죄차량의 위치를 파악, 주변 경찰들에게 연락을 주어 빠르게 범죄차량을 검거하는 서비스를 제공한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.833-838
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• 2003

This paper presents novel methods of recognizing license plates of passing vehicles outdo(n. In particular, the proposed method is much robust for inclined plates caused by the changes of camera placement. To acquire fine images of quickly passing vehicles under a wide range of illumination conditions, we developed a sensing system having superb characteristics. We expanded the dynamic range and eliminated the blurring of images of fast moving vehicles by synthesizing a pair of synchronized images with different intensities. furthermore, to extend the flexibility of the positioning of the TV camera, we propose a recognition algorithm that can be applied to inclined plates. The performance of the integrated system was investigated on real images of vehicles captured under various illumination conditions. The recognition rates of over 99% (conventional plates) and over 97% (highly inclined plates) shows that the developed system is effective for license plate recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.780-785
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• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.101-104
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• 2003

본 논문은 개인용 휴대장비인 디지털카메라등을 통하여 차량의 앞/뒤 번호판을 자동인식하며 인식된 결과를 텍스트 형식으로 결과를 사용자에게 통보함은 물론, 입력된 차량의 정보를 부호화하고 통신망을 통하여 원격지 서버로 전달하고 원격지 서버는 복호화과정을 거쳐 전송된 텍스트 형태의 차량번호를 확인하여 차량에 대한 정보를 제공하는 시스템이다. 이는 급증하는 차량범죄 및 차량통제, 도난차량검거, 수배차량추적등 많은 분야에 효과적으로 사용이 가능하며 무선 및 도로교통에 많은 편의성과 효율성을 제고할 수 있다고 사료된다.