• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.4
• /
• pp.400-404
• /
• 2006

We suggest a method to detect rear car license plate of one more cars by using blobs. First, we try to search all of the blobs from an input image based on the difference between objects and background. Second, we obtain rectangles enclosed the blobs, and rectangle clusters by considering the properties, for example, the number, size, distance, position. Third, the cluster is verified by the Support Vector Machine. Even if we only use the adaptive binarization as the preprocessing, the detection ratio is very high.

• Smart Structures and Systems
• /
• v.7 no.2
• /
• pp.117-132
• /
• 2011

This paper is aimed at presenting two methods on the basis of pattern search and genetic algorithms to detect and estimate damage in plates using the modal data of a damaged plate. The proposed methods determine the damages of plate structures using optimization of an objective function by pattern search and genetic algorithms. These methods have been applied to two numerical examples, namely four-fixed supported and cantilever plates with and without noise in the modal data and containing one or several damages. The obtained results clearly reveal that the proposed methods can be viewed as a powerful and reliable method for structural damage detection in plates using the modal data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.11a
• /
• pp.608-613
• /
• 2003

In this paper, the variability of modal properties caused by temperature effects is assessed to adjust modal data used for frequency-based damage detection in plate-girder bridges. First, experiments on model plate-girder bridges are described. Next, the relationship between temperature and natural frequencies is assessed and a set of empirical frequency-correction formula are analyzed for the test structure. Finally, a frequency-eased method is used to locate and estimate severity of damage in the test structure using experimental modal data which are adjusted by the frequency-correction formula. Here, local damage in beam-type structures is detected by using measured frequencies and analytical mode shapes.

• Journal of the Korean Society for Railway
• /
• v.12 no.3
• /
• pp.364-369
• /
• 2009

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection doe to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Journal of Advanced Technology Convergence
• /
• v.2 no.4
• /
• pp.7-11
• /
• 2023

In this study, we presented a method to recognize vehicle license plates using CNN techniques. A vehicle plate is normally used for the official identification purposes by the authorities. Most regular Optical Character Recognition (OCR) techniques perform well in recognizing printed characters on documents but cannot make out the registration number on the number plates. Besides, the existing approaches to plate number detection require that the vehicle is stationary and not in motion. To address these challenges to number plate detection we make the following contributions. We create a database of captured vehicle number plate's images and recognize the number plate character using Convolutional Neural Networks. The results of this study can be usefully used in parking management systems and enforcement cameras.

• Journal of the Korean Ceramic Society
• /
• v.43 no.4 s.287
• /
• pp.259-263
• /
• 2006

The applicability of UPT (Ultrasound Pulse Thermography) for real-time defect detection of the ceramic heating plate is described. The ceramic heating plate with superior insulation and high radiation is used to control the water temperature in underwater environment. The underwater temperature control system can be damaged owing to the short circuit, which resulted from the defect of the ceramic heating plate. A high power ultrasonic energy with pulse duration of 280 ms was injected into the ceramic heating plate in the vertical direction. The ultrasound excited vibration energy sent into the component propagate inside the sample until they were converted to the heat in the vicinity of the defect. Therefore, an injection of the ultrasound pulse wave which results in heat generation, turns the defect into a local thermal wave transmitter. Its local emission is monitored and recorded via the thermal infrared camera at the surface which is processed by image recording system. Measurements were Performed on 4 kinds of samples, composed of 3 intact plates and the defect plate. The observed thermal image revealed two area of crack in the defective ceramic heating plate.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.453-462
• /
• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3488-3500
• /
• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.872-898
• /
• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Proceedings of the KIEE Conference
• /
• 2007.11c
• /
• pp.191-193
• /
• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.