• Journal of Platform Technology
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• v.9 no.2
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• pp.46-54
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• 2021

Recently, interest in the safety of Self-driving has been increasing. Self-driving have been studied and developed by many universities, research centers, car companies, and companies of other industries around the world since the middle 1980s. In this study, we propose the automatic extraction method of the threatening obstacle on the Road for the Self-driving. A threatening obstacle is defined in this study as a comparatively large object at center of the image. First of all, an input image and its decreased resolution images are segmented. Segmented areas are classified as the outer or the inner area. The outer area is adjacent to boundaries of the image and the other is not. Each area is merged with its neighbors when adjacent areas are included by a same area in the decreased resolution image. The Obstacle area and Non Obstacle area are selected from the inner area and outer area respectively. Obstacle areas are the representative areas for the obstacle and are selected by using the information about the area size and location. The Obstacle area and Non Obstacle area consist of the threatening obstacle on the road. Through experiments, we expect that the proposed method will be able to reduce accidents and casualties in Self-driving.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1314-1321
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• 2010

In this paper, we study about the vehicle detection algorithm which is in the process of travelling from the road. An input image is segmented by means of split and merge algorithm. And two largest segmented regions are removed for reducing search region and speed up processing time. In order to detect the back side of the front vehicle considers a vertical/horizontal component, uses an integral image with to apply Haar-like methods which are the possibility of shortening a calculation time, classified with SVM. The simulation result of the method which is proposed appeared highly.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.151-159
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• 2010

This paper presents a research result that was performed to develop a more accurate freeway crash prediction model than existing models. While the existing crash models only focus on developing crash relationships associated with highway geometric conditions found on a short section of a crash site, this research applies a different approach considering the upstream highway geometric conditions as well. Theoretically, crashes occur while motorists are in motion, and particularly at freeways vehicle speed at one specific point is very sensitive to upstream geometric conditions. Therefore, this is a reasonable approach. To form the analysis data base, this research gathers the geometric conditions of the West Seaside Freeway 269.3 km and six years crash data ranging 2003-2008 for these freeway sections. As a result, it is found that crashes fit well into Negative Binomial Distribution, and, based on the developed model, total number of crashes is inversely proportional to highway curve length and radius. Contrarily, crash occurrences are proportional to tangent length. This result is different from existing crash study results, and it seems to be resulted from this research assumption that a crash is influenced greatly by upstream geometric conditions. Also, this research provides the expected effects on crash occurrences of the length of downgrade sections, speed camera placements, and the on- and off- ramp presences. It is expected that this research result is useful for doing more reasonable highway designs and safety audit analysis, and applying the same research approach to national roads and other major roads in urban areas is recommended.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.123-130
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• 2000

In this paper, we propose an extracting method of moving object contour using active contour model from image sequences acquired by fixed camera. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Noises in boundary area of moving object we eliminated by morphological filter. The contour of segmented object is corrected by using active contour model for extracting accurate boundary of moving object. We apply the proposed method to highway image sequences and show the results of simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.503-506
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• 2012

본 연구에서는 일반 야외 영상 및 항공 시뮬레이션 영상에 대한 지형 분석을 위해 영역 기반 장면 분할 기법을 제시한다. 영역의 분류를 위해 MeanShift 기법을 기반으로 한 표현과 Texton, SIFT, 위치정보를 특징으로 하는 기법을 제안하고 실험을 통해 주요 대상 영역이 분할되는 결과를 보인다. Sowerby 데이터 셋과 Google Earth 데이터로부터 자체적으로 제작한 데이터 셋에 대해 실험하였으며 수풀지형, 초목지형, 도로 등에 대해 분류하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.238-239
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• 2018

본 논문은 기존에 홀로그램 생성에 사용하던 R-S알고리즘과 유사한 품질을 지니며 빠른 속도의 연산으로 홀로그램 디스플레이와 프린팅 시스템을 위한 콘텐츠의 생성에 필요한 고속 연산 알고리즘인 FPAS에 대한 소개이다. 소개 될 연산방법은 기존의 홀로그램 분할 방식을 일반화함으로써 미세한 빔의 조종으로 개선된 알고리즘으로 제안하며, 생성된 프린지 패턴에서 회절된 파면을 R-S알고리즘으로 계산한 결과와 매우 유사한 재구성 품질을 표현할 뿐만 아니라, 각 오브젝트 포인트의 기여도로부터 분할단계에서 보다 빠르게 계산 속도를 향상시킬 수 있는 접근법으로 디지털 홀로그램 생성의 속도를 단축시킬 수 있는 알고리즘을 소개하여 홀로그램을 사용하는 다양한 분야에 응용시키는 것에 목적이 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.248-255
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• 2014

In this paper, we propose a pothole detection method in asphalt pavement using various features. Segmentation, candidate, and decision steps of pothole detection are processed according to the values which are derived from feature characteristics. Segmentation step, we use histogram and closing operation of morphology filter which extracts dark regions for pothole detection. Candidate step, we extract candidate regions of pothole using various features such as size, compactness, etc. Finally, decision step, candidate regions are decided whether pothole or not using comparison of pothole and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination of pothole and similar patterns.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.633-637
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• 2001

In this paper, we proposed model creation algorithm for multiple moving objects tracking. The proposed algorithm is divided that the initial model creation step as moving objects are entered into background image and the model reformation step in the moving objects tracking step. In the initial model creation step, the initial model is created by AND operating division image, divided using difference image and clustering method, and edge image of the current image. In the model reformation step, a new model was reformed in the every frame to adapt appearance change of moving objects using Hausdorff Distance and 2D-Logarithmic searching algorithm. We simulated for driving cart in the road. In the result, model was created over 98% in case of irregular approach direction of cars and tracking objects number.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.91-101
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• 2008

A multi layered elastic analysis program, IDYSPAP, was developed. The objective of this study was to develop the IDYSPAP program on Graphic User Interface environment for field engineers using Visual Basic, which was considered span of multi-wheels and maximum 4 axles using superposition of linear elastic theorem. It is suggested that this study considers algorithm with dynamic properties of asphalt layer on various temperature and non-linear properties of subbase and subgrade on stress non-linearity for asphalt pavement structure. This Program was modified to divide asphalt layer automatically according to layer division concept. The developed program was verified with initial measuring data in test road sections of KEC (Korea Expressway Co.) using laboratory test results.