• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.239-245
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• 2017

For privacy protection, we propose a vehicle license plate region detection method in road view image served from portal site. Because vehicle license plate regions in road view images have different feature depending on distance, long distance vehicle license plate regions are not detected by feature of low resolution. Therefore, we suggest a method to detect short distance vehicle license plate regions by edge feature and long distance vehicle license plate regions using MSER feature. And then, we select candidate region of vehicle license plate region from detected region of each method, because the number of the vehicle license plate has a structural feature, we used it to detect the final vehicle license plate region. As the experiment result, we got a recall rate of 93%, precision rate of 75%, and F-Score rate of 80% in various road view images.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.653-663
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• 2009

In this paper, we propose a blind spots elimination system using three cameras. A wide-angle camera is attached on trunk for eliminating blind spots of a rear-view mirror and two cameras are attached on each side-view mirror for eliminating blind spots of vehicle's sides. In order to eliminate blind spots efficiently, we suggest a method to build a panoramic mosaic view with two side images and one wide-angle rear image. First, we obtain an undistorted image from a wide-angle camera of rear-view and calculate the focus-of-contraction (FOC) in undistorted images of rear-view while the car is moving straight forward. Second, we compute a homography among side-view images and an undistorted image of rear-view in flat road scenes. Next, we perform an image registration process after road and background region segmentation. Finally, we generate various views such as a cylinder panorama view, a top view and an information panoramic mosaic view.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.773-778
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• 1991

In this paper we describe a road following method for an autonomous vehicle. From a road image in gray level, a road boundary is detected using a gradient operator, and then the road boundary is converted to orthogonal view of the road showing the vehicle position and heading direction. In this research an efficient road boundary search technique is developed to support real time vehicle control. Also, an obstacle detection method, using images taken from two different positions, has been developed.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.760-767
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• 2018

Advanced driver assistance system(ADAS) is a major technique in the intelligent vehicle field. The techniques for ADAS can be separated in two classes, i.e., methods that directly control the movement of vehicle and that indirectly provide convenience to driver. In this paper, we propose a novel system that gives a visual assistance to driver by translating a night road image to a day road image. We use the black box images capturing the front road view of vehicle as inputs. The black box images are cropped into three parts and simultaneously translated into day images by the proposed image translation module. Then, the translated images are recollected to original size. The experimental result shows that the proposed method generates realistic images and outperforms the conventional algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.6-49
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• 2001

This paper describes a self-localization method for the mobile robot using panoramic view images. A panoramic view image has the information of location of the objects from the viewer robot and direction between the objects at a position. Among the sequence of panoramic view images, the target objects in the image like traffic signs, facade of a building, road signs, etc. locate in the real world so that robot´s position and direction deliver to localize from his view. With the previously captured panoramic images, the method calculates the distance and direction of the region of interest, corresponds the regions between the sequences, and identifies the location in the world. To obtain the region, vertical edge line segments

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.163-171
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• 2005

Current roads were designed and constructed based on the design criteria and thus those were overly simplified drivers' needs. The road criteria do not suggest the desirable range of the design values but suggest the minimum requirements for the road design. Therefore, a completed road design based on the design criteria does not always guarantee the best design in terms of safety and it sometimes violates drivers' expectation. Therefore, the ROSSAV(ROad Safety Survey and Analysis Vehicle) is being developed by the KICT to evaluate road safety and increase driving safety. In this paper, the image capture system was described in detail. The image capture system is consisted of two front view cameras, two side down-looking cameras and a synchronization device. Two front view cameras were used to take a picture of road and road facilities at the driver's viewpoint. Also, two side down-looking cameras were used to capture road surface image to extract lane markings. A synchronization device were used to generate image capturing signal at the fixed distance spacing huck as every 10m. The front view images could be used to calculate and measure highway geometry such as shoulder width because every image is saved with it's locational information. And also the side down looking images could be used to extract median lane mark which representing road alignement efficiently.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.45-51
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• 2003

Stereo bird's-eyes-view was prepared for 3-dimensional view of various forms of Jecheon city, and 3-dimensional simulation was applied to it so as to show it in moving pictures in spatial. In manufacturing stereo bird's-eyes-view, perspective technology was used in image-making technology, and the basic material images are prepared as fellows: used EOC Images from Arirang-1 satellite, created DEM whose error was optionally geometric corrected after drawn from the contour line of the map on a scale of l/5,000 manufactured by national geography institute as a national standard map, and classified road lines which were manufactured as a road layer vector file of a map on a scale of l/l,000 and then overlay it over the three dimensional image of target area. Especially for the connectivity with address system to be used in new address, an arterial road map on a scale of l/l,000 that had been manufactured to grant new address was used in maximum in road network structure data of city area in this study.