• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.27-34
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• 2017

We propose a cascade lane detector that uses biased discriminant analysis (BDA) to work effectively even when there are various external factors on the road. The proposed cascade detector was designed with an existing lane detector and the detection module using BDA. By placing the BDA module in the latter stage of the cascade detector, the erroneously detected results by the existing detector due to sunlight or road fraction were filtered out at the final lane detection results. Experimental results on road images taken under various environmental conditions showed that the proposed method gave more robust lane detection results than conventional methods alone.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.19-25
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• 2005

This paper presents a lateral control system for the autonomous navigation vehicle that was developed and tested by Robotics Centre of Ecole des Mines do Paris in France. A robust lane detection algorithm was developed for detecting different types of lane marker in the images taken by a CCD camera mounted on the vehicle. $^{RT}Maps$ that is a software framework far developing vision and data fusion applications, especially in a car was used for implementing lane detection and lateral control. The lateral control has been tested on the urban road in Paris and the demonstration has been shown to the public during IEEE Intelligent Vehicle Symposium 2002. Over 100 people experienced the automatic lateral control. The demo vehicle could run at a speed of 130km1h in the straight road and 50km/h in high curvature road stably.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.14-20
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• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

• Archives of design research
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• v.17 no.4
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• pp.223-232
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• 2004

The pedestrian space on the roads shows virtually different images, depending on the local uniqueness that exists in the roadsides, to the one walking. This sort of characteristics of the region originated from the physical special structures of the roadside building the form of the place. Thus, because of the structural difference of the roadside, Pedestrian sense the difference of regions through other images. Research focused on issues of the local roadside sidewalk spaces as what roadside structure is the type that brings out the unique images of the region, and what facets are pursued additionally here, is needed. A roadside of a prosperous region filled with many Pedestrians is selected as the range for the experiment in order to analyze the structure and image of the pedestrian space. Among the roads of the selected region, the structure of the pedestrian space on the roads with more than four lanes was evaluated. As result of the analysis, the images of 10 pedestrian space could be classified into two groups by the difference in proportions of the Df/H(the width of the sidewalk and the height of the roadside building) and the D/H(the width of the road and the height of the roadside building). In order to observe the images of the pedestrian space classified into two groups, the adjectives used to describe the image of scenery were researched, enabling one to induce the images of the two groups form them. One of the images is the image of prosperities, and the other is the image of pleasantness. In addition, as result to the evaluation focused on the characteristic of the roadside buildings in the selected area, it could be divided into two groups, i.e., the commercial region and the business region. The image of prosperities was sensed on the sidewalks of the commercial region, while the image of pleasantness was seen on that of the business region. This study enabled the acknowledgment that in a pedestrian space on a road structure with more than four lanes, the Pedestrian sense different images, depending on the proportional difference in the width of the sidewalk & the height of the roadside building, and the width of the road & the height of the roadside building. This result is expected to be a good reference when a road structure reflecting the uniqueness of its region is to be designed, and especially when the structure of a pedestrian space is to be created.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.192-195
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• 2007

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.107-115
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• 2014

Visibility of the road influence the safe driving because it recognizes the obstacle on the road. In this paper, we propose a mobile data acquisition and processing system for evaluating road visibility at night. And it was converted efficiently with mobile images and archived for spatial analysis of road-visibilities at night. This was applied to the following techniques to the system. Low-power computing units, open an image processing library, GPU-based acceleration techniques and document database techniques, etc. And converting the RGB image to the YUV color system, which was integrated the brightness component and the spatial information. High performance Android devices were used to collect brightness data on roads and it was confirmed whether this prototype was to determine the spatial distribution of such acquisition and management systems for spatial-assessments of road visibility at night.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.87-96
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• 2016

Recently, there is a need for automatic recognition of a variety of symbols on roads because of activation of information services using digital maps on the Web or mobile devices. This paper proposes a method which automatically recognizes 11 kinds of symbolic road markings on the road surface with HOG-SP(Histogram of oriented Gradients-Split Projection) descriptor and shows improvement of lane position detection with recognized symbolic road markings. With the proposed method, recognition rate of 81.99% has been proven on NAVER road view images and the experiments proves the superiority of proposed method by comparisons with other existing methods. Moreover, this paper shows 7.64% higher lane position detection rate by recognizing road surface marking beforehand than only detecting lanes' positions.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.498-506
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• 2003

A computer vision system applied to an intelligent safety vehicle has been required to be worked on a small sized real time special purposed hardware not on a general purposed computer. In addition, the system should have a high reliability even under the adverse road traffic environment. This paper presents a design and an implementation of an onboard hardware system taking into account for high speed image processing to analyze a road traffic scene. The system is mainly composed of two parts: an early processing module of FPGA and a postprocessing module of DSP. The early processing module is designed to extract several image primitives such as the intensity of a gray level image and edge attributes in a real-time Especially, the module is optimized for the Sobel edge operation. The postprocessing module of DSP utilizes the image features from the early processing module for making image understanding or image analysis of a road traffic scene. The performance of the proposed system is evaluated by an experiment of a lane-related information extraction. The experiment shows the successful results of image processing speed of twenty-five frames of 320$\times$240 pixels per second.