• Journal of IKEEE
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• v.23 no.4
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• pp.1337-1342
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• 2019

The Automatic Korean license plate recognition (AKLPR) is used in many fields. For many applications, high recognition rate and fast processing speed of ALPR are important. Recent advances in deep learning have improved the accuracy and speed of object detection and recognition, and CNN (Convolutional Neural Network) has been applied to ALPR. The ALPR is divided into the stage of detecting the LP region and the stage of detecting and recognizing the character in the LP region, and each step is implemented with separate CNN. In this paper, we propose a single stage CNN architecture to recognize license plate characters at high speed while keeping high recognition rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.157-161
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• 2017

This paper introduce UHF miniaturized RFID tag antenna which is embedded on the seal-bolt or plastic bolt for automobile plate. To detect the illegal and un-registered car, the illegal automobile detection system has been developed using the seal-bolt UHF RIF tag antenna. The diameter of seal-bolt UHF tag is about 24mm, almost the same size as 100 Won coin. The simulated and measured reflection coefficient are compared, and the reading range patten is also measured. If seal-bolt tag is embedded on car plate, police can get information of automobile and detect illegal vehicles easily with the illegal automobile detection system.

• Journal of Korean Port Research
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• v.13 no.2
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• pp.303-312
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• 1999

The automated gate operating system is developed in this paper that controls the information of container at gate in the ACT. This system can be divided into three parts and consists of container identifier recognition car plate recognition container deformation perception. We linked each system and organized efficient gate operating system. To recognize container identifier the preprocess using LSPRD(Line Scan Proper Region Detection)is performed and the identifier is recognized by using neural network MBP When car plate is recognized only car image is extracted by using color information of car and hough transform. In the port of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container. Thirdly edge is fitted into line segment so that container deformation is perceived. As a results of the experiment with this algorithm superior rate of identifier recognition is shown and the car plate recognition system and container deformation perception that are applied in real-time are developed.

• Journal of fish pathology
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• v.37 no.1
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• pp.9-15
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• 2024

An enzyme-linked immunosorbent assay (ELISA) with two Novirhabdovirus antigens (viral hemorrhagic septicemia virus, VHSV and infectious hematopoietic necrosis virus, IHNV) was used to detect specific antibodies against VHSV from olive flounder (Paralichthys olivaceus) sera. In ELISA plates with VHSV culture supernatants (VHSV-Ag plate), optical density (OD) values for sera from olive flounder with VHS history (VHS sera) ranged from 0.64±0.36, and those of sera from fish without VHS history (non-VHS sera) ranged from 0.26±0.26. In IHNV-Ag plate, the OD values (0.43±0.28) for VHS sera were quite low compared to those in VHSV-Ag plates, while the OD values for non-VHS sera were almost similar. When the OD values for each serum were calculated by subtracting the OD values in the IHNV-Ag plate from those in the VHSV-Ag plate, the corrected OD values were significantly different between VHS sera and non-VHS sera. The results were completely in line with fish histories of VHS epizootics. It was considered that the corrected OD values may represent the true values recognized by VHSV-specific antibodies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2509-2528
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• 2019

There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.

• 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.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.661-680
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• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.568-574
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• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.