• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.227-228
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• 2014

In this paper, a license plate investigation which is one of parking usage is performed. We survey research that should be given time interval using the camera without the observer. We assigned the investigation time interval to each of to each of the parking car, average parking duration is determined. Parking turnover, the number of vehicles of parking per hour per parking surface, is examined.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.6-10
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• 2021

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.433-440
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• 2023

When the reflector is hit by a vehicle or returned by a storm, an event is generated by the impact sensor and a trigger is operated. The trigger processing algorithm of this paper compares the X, Y, and Z values of the gyro sensor with the registered values and proposes to drive them to the original values by the operation of the 3-axis driving motor. And by recognizing the vehicle license plate, if the vehicle is stolen or a social problem, information is provided to the police operation network. When the reflector is stolen or moved, it has a registered GPS value, so it operates the theft monitoring function to process it.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.465-466
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• 2022

본 논문에서는 딥러닝 영상인식 기술을 활용한 객체검출 모델인 YOLOv4를 활용하여 차량의 모델과 번호판인식 시스템을 제안한다. 본 논문에서 제안하는 시스템은 실시간 영상처리기술인 YOLOv4를 사용하여 차량모델 인식과 번호판 영역 검출을 하고, CNN(Convolutional Neural Network)알고리즘을 이용하여 번호판의 글자와 숫자를 인식한다. 이러한 방법을 이용한다면 카메라 1대로 차량의 모델 인식과 번호판 인식이 가능하다. 차량모델 인식과 번호판 영역 검출에는 실제 데이터를 사용하였으며, 차량 번호판 문자 인식의 경우 실제 데이터와 가상 데이터를 사용하였다. 차량 모델 인식 정확도는 92.3%, 번호판 검출 98.9%, 번호판 문자 인식 94.2%를 기록하였다.

• Korean Journal of Optics and Photonics
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• v.26 no.2
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• pp.88-97
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• 2015

An omnidirectional optical system can be described as a special optical system that images in real time a panoramic image with an azimuthal angle of $360^{\circ}$ and the altitude angle corresponding to the upper and lower fields of view from the horizon line. In this paper, for easy fabrication and compact size, we designed and fabricated a catadioptric omnidirectional optical system consisting of the mirror part of a spherical mirror with a central hole (that is, obscuration), a plane mirror, the imaging lens part of 3 single spherical lenses, and a spherical doublet in the visible light spectrum. We evaluated its image performance by measuring the cut-off spatial frequency using automobile license plates, and the vertical field of view using an ISO 12233 chart. We achieved a catadioptric omnidirectional optical system with vertical field of view from $+53^{\circ}$ to $-17^{\circ}$ and an azimuthal angle of $360^{\circ}$. This optical system cleaniy imaged letters on a car's front license plate at the object distance of 3 meters, which corresponds to a cut-off spatial frequency of 135 lp/mm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.219-231
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• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.655-661
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• 2017

It is a very important task to extract character regions contained in various input color images, because characters can provide significant information representing the content of an image. In this paper, we propose a new method for extracting character regions from various input images using MCT features and an AdaBoost algorithm. Using geometric features, the method extracts actual character regions by filtering out non-character regions from among candidate regions. Experimental results show that the suggested algorithm accurately extracts character regions from input images. We expect the suggested algorithm will be useful in multimedia and image processing-related applications, such as store signboard detection and car license plate recognition.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.496-502
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• 2016

In this paper, we proposed and implemented a GUI-based system to recognize and record measured values of digital instruments in the industrial site through image processing. Unlike the existing vehicle license plate recognition system, the measured values of the measuring instrument are displayed on the LCD screen as digital numbers. So, the proposed system considers the decimal point, a negative sign, light reflected by LCD protective glass, and various disturbance factors. We used blob-labeling technique to recognize the numbers displayed on the LCD screen, the recognized number images were determined as certain numbers through the template matching, and recognized values were recorded in the storage device with measurement time. Therefore, the proposed system in this paper would reduce the burden of writing when recording the measured values of the inside/outside diameter or height of the product in the industrial site, so effective and errorless process management in production process is possible by preventing errors in recording measurements when written by hand.