• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.31-39
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• 2010

The paper proposes the median filter based Retinex algorithm to detect the lanes in a foggy image. Whether an input image is foggy or not is determined by analyzing the histogram in the pre-defined ROI(Region of Interest). If the image is determined as a foggy one, then it is improved by the median filter based Retinex algorithm. By replacing the Gaussian filter by the median filter in the Retinex algorithm, the processing time can be reduced and the lane features can be detected more robustly. Once the enhanced image is acquired, the binarization based on multi-threshold and the labeling operations are applied. Finally, it detects the lane information using the size and direction parameters of the detected lane features. The proposed algorithm has been evaluated by using various foggy images collected on different road conditions to prove that it detects lanes more robustly in most cases than the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.349-354
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• 2015

This paper is about utilizing magnetic sensor to measure magnetic signal and analyze the form of magnetic signal for vehicle detection. For magnetic sensor, MR sensor from Honeywell company was used, and Helmholtz coil of which 3 axis' length is 1.2 m was manufactured to check the capability of the sensor and estimate its ability to detect the magnetic field. Vehicle detection was performed in following steps: installing sensor in road lane and non-road lane; estimating magnetic field when the vehicle is run by the driver; and estimating magnetic field of 7 different vehicles with different sizes. Also, sensor was installed at SUV and small-sized vehicle's park and non-park area to analyze the form of magnetic field. Lastly, the form of magnetic field made by different parts of the vehicle was analyzed. Based on the analysis, the form of magnetic field's magnetic peak value was bigger for road lane than non-road lane, complicated form was useful to distinguish the road lane above the installed sensor and the location of the running car, and the types of vehicle could be sorted because the variance of the magnetic field was bigger for bigger size of the vehicle. Also, it was confirmed that the forms of vehicle in parts-by-parts estimates.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.73-83
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• 2019

In this paper, we propose the augmented reality(AR) based driving navigation based on robust lane detection method to dynamic environment changes. The proposed technique uses the detected lane position as a marker which is a key element for enhancing driving information. We propose Symmetrical Local Threshold(SLT) algorithm which is able to robustly detect lane to dynamic illumination environment change such as shadows. In addition, by using Morphology operation and Connected Component Analysis(CCA) algorithm, it is possible to minimize noises in the image, and Region Of Interest(ROI) is defined through region division using a straight line passing through several vanishing points We also propose the augmented reality aided visualization method for Interchange(IC) and driving navigation using reference point detection based on the detected lane coordinates inside and outside the ROI. Validation experiments were carried out to assess the accuracy and robustness of the proposed system in vairous environment changes. The average accuracy of the proposed system in daytime, nighttime, rainy day, and cloudy day is 79.3% on 4600 images. The results of the proposed system for AR based IC and driving navigation were also presented. We are hopeful that the proposed research will open a new discussion on AR based driving navigation platforms, and thus, that such efforts will enrich the autonomous vehicle services in the near future.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.1
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• pp.115-125
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• 2019

In this paper, the image is received from the camera and the lane is sensed. There are various ways to detect lanes. Generally, the method of detecting edges uses a lot of the Sobel edge detection and the Canny edge detection. The minimum use of multiplication and division is used when designing for the hardware configuration. The images are tested using a black box image mounted on the vehicle. Because the top of the image of the used the black box is mostly background, the calculation process is excluded. Also, to speed up, YCbCr is calculated from the image and only the data for the desired color, white and yellow lane, is obtained to detect the lane. The median filter is used to remove noise from images. Intermediate filters excel at noise rejection, but they generally take a long time to compare all values. In this paper, by using addition, the time can be shortened by obtaining and using the result value of the median filter. In case of the Sobel edge detection, the speed is faster and noise sensitive compared to the Canny edge detection. These shortcomings are constructed using complementary algorithms. It also organizes and processes data into parallel processing pipelines. To reduce the size of memory, the system does not use memory to store all data at each step, but stores it using four line buffers. Three line buffers perform mask operations, and one line buffer stores new data at the same time as the operation. Through this work, memory can use six times faster the processing speed and about 33% greater quantity than other methods presented in this paper. The target operating frequency is designed so that the system operates at 50MHz. It is possible to use 2157fps for the images of 640by360 size based on the target operating frequency, 540fps for the HD images and 240fps for the Full HD images, which can be used for most images with 30fps as well as 60fps for the images with 60fps. The maximum operating frequency can be used for larger amounts of the frame processing.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.57-69
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• 2000

A moving window technique for detecting a lane and obstacles using the Images captured by a CCD camera attached in an automobile, is proposed in this paper To process the dynamic images in real time, there could be many constraints on the hardware To overcome these hardware constraints and to detect the lane and obstacles in real time, the optimal size of window IS determined based upon road conditions and automobile states. By utilizing the sub-Images inside the windows, detection of the lane and obstacles become possible m real time. For each Image frame, the moving windows are re-determined following the predicted directions based on Kalman filtering theory to Improve detection accuracy, as well as efficiency The feasibility of proposed algorithm IS demonstrated through the simulated experiments of highway driving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.538-539
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• 2018

This paper summarizes the implementation of lane recognition using OpenCV, one of the open source computer vision libraries. The Linux operating system Rasbian(r18.03.13) was installed on the ARM processor-based Raspberry Pi 3 board, and Raspberry Pi Camera was used for image processing. In order to realize the lane recognition, Canny Edge Detection and Hough Transform algorithm implemented in OpenCV library was used and RANSAC algorithm was used to prevent shaking of vanishing point and to detect only the desired straight line. In addtion, the DC motor and the Servo motor were controlled so that the vehicle would run according to the detected lane.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.311-317
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• 2011

The blind spot is an area where drivers visibility does not reach. When drivers change a lane to adjacent lane, they need to give an attention because of the blind spot. If drivers try to change lane without notice of vehicle approaching in the blind spot, it causes a reason to have a car accident. This paper proposes a night-time blind spot vehicle detection using cameras. At nighttime, head-lights are used as characteristics to detect vehicles. Candidates of headlight are selected by high luminance feature and then shape filter and kalman filter are employed to remove other noisy blobs having similar luminance to head-lights. In addition, vehicle position is estimated from detected head-light, using virtual center line represented by approximated the first order linear equation. Experiments show that proposed method has relatively high detection porformance in clear weather independent to the road types, but has not sufficient performance in rainy weather because of various ground reflectors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.834-841
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• 2020

Among the autonomous driving systems based on visual sensors, the control method using a vanishing point is the most general method for autonomous driving. However, if the lane is lost or does not exist, it is very difficult to detect this and estimate the vanishing point. In this paper, we predict the vanishing point of the road and the vanishing point lines on the left and right sides using CNN for the camera image and design the steering controller for autonomous driving from the predicted results. As a result of the simulation, it was confirmed that the proposed method well tracked the center of the road regardless of the presence or absence of a solid lane, and was superior to the control method using a general method using the vanishing point.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.96-100
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• 2015

Adaptive background gain calibration loop for multi-lane serial links is proposed. In order to detect and cancel gain mismatches between lanes, a single digital loop using a ${\sum}{\Delta}$ ADC is employed, which provides a real-time adaptation of gain variations and is shared among all lanes to reduce power and area. Evaluation result showed that gain mismatches between lanes were well calibrated and tracked, resulting in timing budget at $10^{-6}$ BER increased from 0.261 UI to 0.363 UI with stable loop convergence.