• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.504-506
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• 2012

운전자 지원 시스템(ADAS)은 최근 지능형 자동차 분야에서 중요한 이슈로 손꼽히는 기술 중 하나이다. 이 중에서 실시간 표지판 인식 기술은 운전자 지원 시스템의 하나로 운전자의 안전과 직결될 수 있어 높은 정확성과 실시간성이 요구된다. 그동안 표지판 인식 분야는 색상과 현상을 기반으로 연구가 진행되어왔으나, 교통 표지판은 국가별로 그 특징과 형태가 각기 상이하여 적용하는데 한계가 있다. 본 논문에서는 한국의 속도제한 표지판을 실시간으로 검출하고 인식하기 위하여, 1) 영상에서 색상 특징을 이용하여 후보 영역을 검출하고, 2) 형상 정보를 분석하여 표지판의 형태를 검증하고, 3) 검출된 후보영역의 내부문자(숫자)를 분할하고 인식하는 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.789-792
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• 2001

본 논문은 자동차의 번호판 인식 시스템의 한 부분인 번호판 추출과 자모 분리를 통한 문자 인식까지의 과정을 실험한 것이다. 본 논문은 gray-level에서 영상을 실험하였고, 번호판을 추출하기 위해서 morphology를 반복 적용하고 크기 보정을 통해 번호판을 추출하며, hough transform을 이용한 크기 재보정을 통해 최종적으로 번호판을 추출한다. 그리고, 문자 인식 단계에서는 먼저 hough transform을 사용하여 한글의 모음의 시작점을 얻고, 문자 특징을 이용하여 자음과 모음을 분리하여 모음을 인식한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.160-164
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• 2005

The process of recognizing a vehicle involves detection of the vehicle, recognition of the vehicle model, and identification of the vehicle. The process of vehicle identification involves identification of the vehicle itself, such as by recognition of the license plate on the vehicle. In this paper the method involves the use of a beam splitter to divide incident rays into two directions, a transmitted beam and a reflected beam of different light intensities, and synthesizing two captured images using CCD devices from each beam, thus producing fluctuation-free images of a wide dynamic range even when the subject is moving. A prototype license plate recognition system was also developed using the experimental sensing device. The system achieved a 98.7% recognition rate on 466 images of moving vehicles, which demonstrates its effectiveness as a license plate recognition system.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.377-382
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• 2006

In this paper, two methods based on the use of morphology and pattern matching prior to detect classified defects automatically on the back light unit which is a part of display equipments are proposed. One is the template check method which detects small size defects by using closing and opening method, and the other is the block matching method which detects big size defects by comparing with four regions of uniform blocks. The TC algorithm also can detect defects on the non-uniform pattern of BLU by using revised Otsu method. The proposed method has been implemented on the automatic defect detection system we developed and has been tested image data of BLU captured by the system.

• Journal of IKEEE
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• v.21 no.2
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• pp.142-145
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• 2017

Currently, a trend in image processing is high-quality and high-resolution. The size and amount of image data are increasing exponentially because of the development of information and communication technology. Thus, license plate recognition with a single processor cannot handle the increasing data. This paper proposes a number plate recognition system using a distributed processing framework, Hadoop. Using SequenceFile format in Hadoop, each mapper performs a license plate recognition with a number of image data in a data block Experimental results show that license plate recognition performance with 16 data nodes accomplishes speedup of maximum 14.7 times comparing with one data node. In large dataset, the recognition performance is robust even if the number of data nodes increases gradually.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.713-725
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• 2019

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.830-833
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• 2016

자동차 번호판 인식은 대중적인 감시 기술 중의 한 종류로서, 주어진 비디오나 영상 내 광학문자 인식을 수반한다. 고속도로나 국도 상에 과속 단속 시스템, 재형 건물이나 유통센서 및 주차장 등에서 주차 정산 시스템, 고속도로 톨 게이트에서 hi-pass 에러 및 불법 도주 차량 잔속 시스템, 전국 주요 도로 불법 주 정차 단속 시스템, 공공기관, 기업 출퇴근 시간 확인 및 외부 차양 안내 시스템 등의 지능형 교통 시스템(ITS)이나 국도 상에 범위 차량 검거 시스템, 사건 발생 시 주요 도로상에 설치된 CCTV를 통해 용의 차량 이동 추적 시스템, 이동식 범죄 차량 조회, 버스에 탑재된 버스 전용차선 위반 단속들의 지능형 방범 시스템 등에 활용하고 있다. 번호판 인식은 자동차 번호판 국부화, 번호판의 크기, 차원, 명암대비, 밝기를 조정하는 정규화, 개별문자를 얻어내는 문자 분할, 문자를 인식하는 광학 문자 인식, 번호판의 형태, 크기, 위치 들이 연도별, 지역별로 차이가 있는 번호판들의 데이터베이스를 비교하여 구문 분석을 하는 절차를 거친다. 본 논문에서는 EmguCV를 이용하여 구현한 번호판 감지를 수행하여 위치를 찾아내고, 오픈 소스 광학 문자 인식 엔진으로 잘 알려져 있는 테서렉트 OCR을 이용하여 번호판의 문자를 인식하는 자동 인식 프로그램을 구현하고 기술하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.467-471
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• 2005

본 논문에서는 명암도 변화 및 윤곽선 추적 알고리즘과 개선된 오류 역전파 알고리즘을 이용한 차량 번호판 인식 방법을 제안한다. 비영업용 차량 영상을 대상으로 차량 번호판 영역을 추출하기 위해 명암도 변화 특성을 이용하여 차량 번호판 영역을 추출한다. 추출된 차량 번호판 영역에 반복 이진화 방법을 적용하여 차량 번호판의 영역을 이진화하고, 이진화된 차량 번호판 영역에 대해서 윤곽선 추적 알고리즘을 적용하여 개별 코드를 추출한다. 추출된 개별 코드 인식은 일반화된 델타 학습 방법에 Delta-bar-Delta 알고리즘을 적용하여 학습률을 동적으로 조정하는 개선된 오류 역전파 알고리즘을 적용한다. 제안된 방법의 인식 성능을 평가하기 위하여 실제 비영업용 차량 번호판에 적용한 결과, 기존의 차량 번호판 인식 방법보다 효율적인 것을 확인하였다.