• 대한조선학회논문집
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• 제59권2호
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• pp.125-133
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• 2022

This paper proposes a fault detection method for a Unmanned Surface Vehicle (USV) with overactuated system. Current status information for fault detection is expressed as a scalogram image. The scalogram image is obtained by wavelet-transforming the USV's control input and sensor information. The fault detection scheme is based on Convolutional Neural Network (CNN) algorithm. The previously generated scalogram data was transferred learning to GoogLeNet algorithm. The data are generated as scalogram images in real time, and fault is detected through a learning model. The result of fault detection is very robust and highly accurate.

• 한국군사과학기술학회지
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• 제26권5호
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• 한국도로학회논문집
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• 제10권3호
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• pp.19-31
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• 2008

영상처리시스템(VIPS: Video Image Processing System)은 실시간으로 들어오는 영상정보를 분석하여 유용한 정보를 제공하며, 하나의 카메라로 여러 차로를 동시에 감시할 수 있는 알고리즘으로 교통량, 속도뿐만 아니라 밀도 및 점유율 등 다양한 정보를 제공한다. 영상검지시스템으로 상용화 제품은 Tripwire시스템으로 검지영역의 픽셀 변화량으로 차량검지를 하나, 이는 교통량, 속도 등 단편적인 정보에 국한될 수 밖에 없다. 반면, 영상검지시스템이 개별차량에 대한 추적시스템으로 개발할 경우 사고 및 차로 변경의 위험요소 감지 등 보다 다양한 정보를 제공할 수가 있다. 본 논문은 컴퓨터비전 기술을 이용하여 Tripwire에서 수집할 수 있는 교통정보와 동일한 정보를 제공하는 개별차량의 추적시스템을 개발하였으며 이 시스템을 실제 도로영상에 적용하여 상용화된 시스템과 결과를 비교함으로써 성능검증을 하였다.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• 한국멀티미디어학회논문지
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• 제19권6호
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• 한국컴퓨터정보학회논문지
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• 제17권1호
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• pp.71-80
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• 2012

본 논문에서는 단일 카메라를 이용하여 차량의 위치를 검출하고 연속적인 프레임에서의 차량의 움직임을 추적하는 알고리즘을 제안한다. 차량의 특징을 검출하기 위해 Haar-like 에지 검출기를 사용하고, 카메라의 캘리브레이션 정보를 이용하여 차량의 위치를 추정한다. 신뢰도를 높이기 위해 k 개의 연속적인 프레임에서의 누적된 차량 정보를 추출한다. 최종 검출된 차량을 템플릿으로 지정하고 SURF (Speeded Up Robust Features) 알고리즘을 통해 연속적으로 입력되는 프레임에서 동일한 차량을 추출한다. 이를 통해 동일 차량으로 추출된 차량 정보를 새로운 템플릿으로 업데이트 한다. 비교 검출을 위한 수행 시간을 줄이기 위해 이전 프레임에서 검출된 차량의 범위를 확장한 영역만을 관심 영역으로 지정한다. 이 과정은 공통된 대응점을 찾지 못할 때까지 검출과 추적 과정을 반복하여 진행한다. 실 도로 상에서 얻어진 영상에 대해 적용함으로써 제안된 알고리즘의 효율성을 보였다.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1416-1418
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• 2004

This paper proposes an image processing algorithm for detecting obstacles on road-lane using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using single lane information from road lane detection. For the purpose to reduce processing time, we use small blocks obtained by edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.509-516
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• 2004

This paper proposes an image processing algorithm for detecting obstacles on road using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using a single lane information from road lane detection. For the purpose to reduce processing time, we use small block of edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

• 한국ITS학회 논문지
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• 제7권4호
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• pp.49-60
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• 2008

현재 비디오 영상처리시스템(VIPS: Video Image Processing System)은 실시간으로 들어오는 영상정보를 분석하여 유용한 정보를 제공하고, 하나의 카메라로 여러 차로를 동시에 감시할 수 있는 알고리즘으로 교통량, 속도뿐만 아니라 밀도 및 점유율 등 다양한 정보를 제공하나, 안전지대에서는 효과적이지 못한다. 그러나, 영상검지시스템에서 개별차량에 대한 추적시스템으로 개발할 경우 사고 및 차로 변경의 위험요소 감지 등 실시간으로 보다 다양한 정보를 제공할 수가 있다. 본 논문은 컴퓨터비전 기술을 이용하여 개별차량의 추적시스템을 개발하였으며, 이 시스템을 실제 도로영상에 적용하여 Tripwire에서 수집할 수 있는 교통정보뿐만 아니라 사고, 상충정보 등 다양한 정보를 제공한다. 본 연구의 검증을 위하여 개별차량 추적시스템으로 1) 돌발상황 감지 2) 급차로 변경과 같은 비정상적인 차량흐름의 경우를 감지하는 실험을 수행하였다.