• Title/Summary/Keyword: 자동차 번호판

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Recognition of Numeric Characters in License Plate based on Independent Component Analysis (독립성분 분석을 이용한 번호판 숫자 인식)

  • Jeong, Byeong-Jun;Kang, Hyun-Chul
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.2
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    • pp.99-107
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    • 2009
  • This paper presents an enhanced hybrid model based on Independent Component Analysis(ICA) in order to features of numeric characters in license plates. ICA which is used only in high dimensional statistical features doesn't consider statistical features in low dimension and correlation between numeric characters. To overcome the drawbacks of ICA, we propose an improved ICA with the hybrid model using both Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA). Experiment results show that the proposed model has a superior performance in feature extraction and recognition compared with ICA only as well as other hybrid models.

Image Processing Algorithm for Vehicle Detection at Blind Spot (사각 지역 차량 감지 영상 처리 알고리즘)

  • Seo, Jiwon;Kwak, Nojun
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2010.11a
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    • pp.67-69
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    • 2010
  • 최근 자동차 업계와 IT 기술의 융합이 새로운 트렌드로 자리 잡으면서 전자제어 기술뿐만 아니라 영상처리 기술이 융합된 지능형 자동차 개발에 대한 연구가 활발히 진행되고 있다. 차선 또는 번호판을 대상으로 하는 인식 알고리즘은 이미 다양한 방법으로 연구가 진행되어 왔으며 이미 몇몇 기술은 상용화 단계에 있다. 본 논문에서는 Viola-Jones 알고리즘을 이용하여 차량의 사각 지대에 위치하는 차량을 감지하고 이의 대략적인 거리 정보를 추정하는 것을 목표로 하여 차량의 형태 정보를 바탕으로 차량을 감지하는 알고리즘을 제안한다. 기본적인 방법은 Adaboost와 Harr-like 특징을 사용하여 얼굴을 성공적으로 검출한 Viola-Jones 알고리즘[1]을 차량에 적용하였다.

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A Study on Road Traffic Volume Survey Using Vehicle Specification DB (자동차 제원 DB를 활용한 도로교통량 조사방안 연구)

  • Ji min Kim;Dong seob Oh
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.2
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    • pp.93-104
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    • 2023
  • Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

2D Artificial Data Set Construction System for Object Detection and Detection Rate Analysis According to Data Characteristics and Arrangement Structure: Focusing on vehicle License Plate Detection (객체 검출을 위한 2차원 인조데이터 셋 구축 시스템과 데이터 특징 및 배치 구조에 따른 검출률 분석 : 자동차 번호판 검출을 중점으로)

  • Kim, Sang Joon;Choi, Jin Won;Kim, Do Young;Park, Gooman
    • Journal of Broadcast Engineering
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    • v.27 no.2
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    • pp.185-197
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    • 2022
  • Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

Enhanced Fuzzy Binarization Method for Car License Plate Binarization (자동차번호판 이진화를 위한 개선된 퍼지 이진화 방법)

  • Cho, Jae-Hyun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.6 no.2
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    • pp.231-236
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    • 2011
  • The binarization algorithm frequently applies to one part of the preprocessing phase for a variety of image processing techniques such as image recognition and image analysis, etc. So it is important that binarization algorithm is determined by the selection of threshold value for binarization in image processing. The previous algorithms could get the proper threshold value in the case that shows all the difference of brightness between background and object, but if not, they could not get the proper threshold value. In this paper, we propose the efficient fuzzy binarization method which first, segments the brightness range of gray_scale images to 2 intervals to perform car license plate binarization and applies fuzzy member function to each intervals. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms in car license plate.

An effective object segmentation on the color plane using Fisher Linear Discriminant (Fisher 선형 분리자를 사용한 컬러 평면에서의 효과적인 목표물 추출)

  • Nahm, Jin-Woo
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2005.11a
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    • pp.213-216
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    • 2005
  • 자동차 번호판의 이미지에서 번호의 추출이나, 직물 이미지에서 오염 또는 훼손부분의 추출 또는 방사성 폐기물이나 기타 독극물 보관함의 이미지에서 오염이나 산화에 의한 훼손부위 등과 같은 목표물 이미지 추출은 흑백 이미지에서 명암의 차이를 이용하는 것보다는 컬러 이미지에서 색상의 차이를 이용하는 것이 더 효율적일 때가 많으며, 특히 배경과 목표물의 명암차이가 크지 않은 경우에 효과적이다. 배경과 목표물이 갖는 색상의 차이를 이용하여 분리하기 위해서 적색(R), 녹색(G), 청색(B) 의 RGB 평면 또는 순도(H), 포화도(S), 휘도(I)를 사용하는 HSI 컬러 평면 등이 많이 사용되며, 이 때 배경과 목표물의 색상의 히스토그램을 구해보면 보면 많은 경우 유사한 색 정보가 배경과 목표물에 공통으로 포함되어 분리에 어려움을 겪게 된다. 본 논문에서는 Fisher 선형 분리자(Fisher's linear discriminant)[1] 함수를 이용하여 3차원의 색상 특징 벡터를 1차원 직선에 투사하여 변환된 1차원 공간상에서 복잡성을 줄이고 효과적으로 분류할 수 있는 기법을 제안하였으며, 이를 도축된 식용 가금류의 영상에 적용하고 변질된 부분이 포함되어 식용으로 사용할 수 없는 것들을 효과적으로 분류할 수 있음을 보였다.

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Constructing Database and Social Experiment of Scenic Byway Using the Multi-Transportation of Korea and Japan (복합교통수단을 이용한 한·일 Scenic Byway의 DB구축 및 실현에 대한 과제)

  • Hwang, In-Sik;Baek, Tae-Kyung
    • Journal of the Korean Association of Geographic Information Studies
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    • v.14 no.3
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    • pp.11-21
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    • 2011
  • This study intends to construct scenic byway database and to examine and suggest social experiment of scenic byway. As basis work for the experiment of the scenic byway, we build database by using ITS standard node link management system. The DB includes scenic byway routes of Korea and Japan. The analyses show that the scenic byway in both nations consists of roads, reservation, road sign, vehicle number plate, and it was found that infrastructure and system are inadequate for scenic byway. These experiment can be effectively used for scenic byway in Korea and Japan as the basis data. The results of this experiment will be useful for plan and develop a scenic byway.

Improvement on Learning Performance of Neural Networks for Extracting Nonlinear Features (비선형 특징추출을 위한 신경망의 학습성능 개선)

  • 조용현;윤중환;성주원
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.11a
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    • pp.77-80
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    • 2000
  • 본 논문에서는 새로운 학습알고리즘의 비선형 주요성분분석 신경망을 이용한 데이터의 효율적인 특징추출에 대하여 제안하였다. 제안된 학습알고리즘에서는 모멘트와 동적터널링을 조합하여 이용함으로써 최적해로의 수렴에 따른 발진을 억제하고 빠른 수렴속도로 전역최적해에 수렴되도록 학습시킬 수 있다. 제안된 학습알고리즘을 이용하여 128$\times$128 픽셀의 얼굴영상과 256$\times$128 픽셀의 자동차번호판 영상을 대상으로 시뮬레이션 한 결과, 기울기하강의 학습알고리즘을 이용한 기존 비선형 주요성분분석 신경망보다 우수한 수렴성능과 특징추출성능이 있음을 확인 할 수 있었다.

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License Plate Recognition Using The Morphological Size Distribution Functions (형태학적 크기 분포 함수를 이용한 자동차 번호판 인식)

  • 차상혁;김주영;고광식
    • Proceedings of the IEEK Conference
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    • 2001.09a
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    • pp.455-458
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    • 2001
  • In this paper, a new license plate recognition method using the morphological size distribution functions and color images is proposed. The proposed method consists of two steps. The first step is license plate extraction process using the plate color and step edge information in the license plate. The second step is the extraction of character feature vectors using the morphological size distribution functions and character recognition process using the MLP(multilayer perceptron). By the use of morphological size distributions functions, the error that may occur during the character region extraction process is lessened and the recognition performances are improved by the decrease of feature vector dimension.

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An ergonomic study on the car license plate in Korea (우리나라 자동차 번호판의 인간공학적 개선에 관한 연구)

  • 박영택;강현준
    • Journal of the Ergonomics Society of Korea
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    • v.14 no.2
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    • pp.15-24
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    • 1995
  • The purpose of this study is to provide ergonomic research data for the redesign of the car license plate in Korea. Several alternatives having various combinations of fonts, stroke-width ratios, color contrasts, and arrays were considered in order to find the best one in terms of the reading-distance and the misreading-rate. Experiments investigating the reading-distance, misreading-rates were conducted. In addition, the alternatives were tested in a real driving situation. The results can be summarized as follows : The typography having mixed type numeric forms with quasi-Gothic and NAMEL, stroke-width ratio of 1:7 .approx. 1: 8, the black on yellow contrast showed a good reading-distance. The array with Hangul(showing territorial office and use-sign) at the upper row and 6 mumbers ( showing car class-sign and serial number) at the lower row showed relatively low misreading-rate.

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