• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.841-843
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• 2012

본 논문에서는 안드로이드 기반의 태블릿 PC 환경에서 차량사고 발생 시 번호판 인식을 통하여 해당 사건의 증인을 확보 할 수 있는 증인 확보용 블랙박스 시스템을 제안한다. 이 방법은 기존의 블랙박스 기능에 추가적으로 영상에서 추출한 자동차들의 번호판을 인식하여 번호판 정보를 로그데이터로 저장하는 방식을 사용한다. 이로 인해 차량 사고에 대한 증인을 확보할 수 있는 시스템을 제공함으로써 사건에 대한 사용자의 불리한 입장을 완화 시켜줄 수 있는 객관적 데이터를 제공 및 저장하는 것을 목표로 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1153-1164
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• 2012

In this paper, we propose an efficient car management system based on object-oriented modeling using car number recognition and smart phone. The proposed system perceives car number of repair vehicle after recognizing the licence plate using an IP camera in real time. And then, existing repair history information of the recognized car is be displayed in DID. In addition, maintenance process is shooting video while auto maintenance mechanic repairs car through IP-camera. That will be provide customer car identification and repairs history management function by sending key frames extracted from recorded video automatically. We provide user graphic interface based on web and mobile for your convenience. The module design of the proposed system apply software design modeling based on granular object-oriented considering reuse and extensibility after implementation. Car repairs center and maintenance companies can improve business efficiency, as well as the requested vehicle repair can increase customer confidence.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.73-81
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• 2007

This paper presents how to recognize the new-type vehicle license plate using multi-link recognizer after extract the features from characters. In order to assist this task, this paper proposed FE-MCBP to recognize each character that got through image preprocess, extract range of vehicle license plate and extract process of each character. FE-MCBP is the recognizer based on the features of the character, The recognizer is employed to identify the new-type vehicle licence plates which have both the hangul and the arabic numeral characters. And its recognition rate is improved 9.7 percent than the back propagation recognizer before. Also it makes use of extract of linear component and region coordinate generation technology to normalize a image of the tilted vehicle license plate. The recognition system of the new-type vehicle license plate make possible recognize a image of the tilted vehicle license plate when using this system. Also, this system can recognize the tilted or imperfect vehicle licence plates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.797-800
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• 2003

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 revered by the learning pattern, the effort 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%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.105-109
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• 2008

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plate.s 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%.

• Journal of IKEEE
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• v.15 no.4
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• pp.274-280
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• 2011

The implementation of the recognition system of a vehicle license plate and the Linux OS environment which is built in SoC Embedded system and its test result are presented in this paper. In order to recognize a vehicle license plate, each character has to be extracted from the whole image of a license plate and the extracted image is revised for the template matching. Labeling technique and numerical features are used to detect the vehicle license plate. Each character in the license plate has coordinates. The extracted image is revised by comparison of the numerical coordinates and recognized through template matching method. The experimental results show that the license plate detection rate is 96%, and a character recognition rate is 73%, and a number recognition rate is 97% for about 300 license plate images. The average time of the recognition in the embedded board is 0.66 sec.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.31-39
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• 2018

In this paper, we proposed a license plate detection method based on contour extraction that adapts to environmental changes. The proposed method extracts contour lines using DoG (Difference of Gaussian) to remove unnecessary noise parts in the contour extraction process. Binarization was applied in ugly outline images, and erosion and dilation operations were used to emphasize the contour of the character part. Then, only the outline of the ratio of the characters of the plate was extracted through the ratio of the width and height of the characters. And the case where the outline is the longest is estimated by estimating the characters of the license plate. For the experiment, we applied 130 image data to license plate on the front of the vehicle, oblique environment, and environment images with various backgrounds. I also experimented with motorcycle images of different license plate patterns. Experimental results showed that the detection rate of the oblique image was 93% and that of the various background environment was 70% in the motorcycle image but 98% in the front image.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.733-735
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• 2018

최근의 차량 주차관리 시설, 출입통제가 필요한 장소 그리고 도로 방범카메라를 통한 단속 등 다양한 곳에서 차량 번호판 자동 인식 기술들이 활용되고 있다. 하지만 현재 사용되고 있는 LPR(License Plate Recognition) 시스템에는 많은 장비와 비용이 들어간다는 큰 단점이 존재한다. 본 논문에서는 하나의 컴퓨터와 최소의 카메라를 가지고 할 수 있는 기계학습을 통한 영상처리를 제안하려 한다. 먼저 딥러닝 프레임워크 중 하나인 YOLO(You Only Look Once) [4]를 활용하여 자동차의 번호판 부분의 영역을 검출하고 Grayscale를 통해 햇빛 또는 조명 등의 영향을 감소시켜 번호판의 특징을 보존시킨다. 전처리 작업이 끝난 후 번호판에서 숫자를 인식 하는 부분에서는 k-NN(k-Nearest Neighbor) 알고리즘을 사용하였으며 한글 문자 인식부분은 Template Matching을 이용하였다. 제안한 알고리즘을 사용하여 기존 LPR 시스템에서 획득한 차량이미지를 대상으로 시뮬레이션 한 결과 좋은 결과를 얻을 수 있어 향후 연구 방향의 시스템 확장성의 가능성을 발견할 수 있었다.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.47-55
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• 2019

In this paper, we propose a method for vehicle plate detection using depth information which is not influenced by illumination. The 3D camera coordinates of pixels in each block are obtained by using the depth information. Factors of the plane in the block are calculated by 3D coordinates of pixels. After that, the plane similarity between adjacent blocks is calculated by comparing between factors of planes. The adjacent blocks are grouped if the plane similarity is high so that the plane areas are detected. The actual height and width of the plane area are calculated by using depth information and compared with the vehicle plate in order to detect the vehicle plate.

• Korea Information Processing Society Review
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• v.14 no.1
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• pp.108-114
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• 2007