• 한국정보통신학회논문지
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• 제25권8호
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• pp.1117-1123
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• 2021

객체 인식이란 하나의 특정 이미지를 입력했을 때, 주어진 이미지를 분석하여 특정한 객체(object)의 위치(location)와 종류(class)를 파악하는 것이다. 최근 객체 인식 기술이 적극적으로 접목되는 분야 중 하나는 자율주행 차량이라 할 수 있고, 본 논문에서는 자율주행 차량에서 영상 기반의 객체 인식 인공지능 기술에 대해 기술한다. 영상 기반 객체 검출 알고리즘은 최근 두 가지 방법(단일 단계 검출 방법 및 두 단계 검출 방법)으로 좁혀지고 있는데, 이를 중심으로 분석 정리하고자 한다. 두 가지 검출 방법의 장단점을 분석 제시하고, 단일 단계 검출 방법에 속하는 YOLO/SSD 알고리즘과 두 단계 검출 방법에 속하는 R-CNN/Faster R-CNN 알고리즘에 대해 분석 기술한다. 이를 통해 자율주행에 필요한 각 객체 인식 응용에 적합한 알고리즘이 선별적으로 선택되어 연구개발 되어질 수 있기를 기대한다.

• Smart Structures and Systems
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• 제15권1호
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.978-983
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• 2003

In this paper, we propose a fault detection method for extended Kalman filter in decentralized filter structure. To detect a fault, a consistency between filter output and a monitoring signal is tested. State propagators are used to obtain the monitoring signal. However, the output of state propagator increases in magnitude and finally diverges as time runs. To solve such problem, two-propagator method was proposed for linear system. Two propagators are reset by Kalman filter output, alternatively, to avoid divergence. But a test statistics change abruptly at the reset instant in that method. Hence a N-step propagator method is proposed to fix up the problem. In the N-step propagator, only time propagations are performed from k-N+1 step to k step without measurement updates. A test statistics are defined by errors and its covariance between extended Kalman filter and N-step propagator. These fault detection methods are applied to integrated strapdown inertial navigation system (SDINS). By computer simulation, it is shown that the proposed methods detect a fault effectively.

• International Journal of Automotive Technology
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• 제8권4호
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• pp.513-520
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• 2007

In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

• 한국멀티미디어학회논문지
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• 제21권11호
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• pp.1244-1253
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• 2018

The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1949-1952
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• 2004

GPS is widely used for outdoor positioning in many applications. But it is not suitable for positioning in an obstacle environment such as urban area, tunnels and so on, due to variable signal level. So new technology of the positioning is required to provide the consistent error level regardless of any changes in any environment. Abrupt changes of GPS signal can be detected by various fault detection and isolation methods. Conventional FDI (Fault Detection and Isolation) methods are categorized into two approaches. One approach is the snapshot method that uses measurements only at present step. The other approach is the filtering method that uses measurements stacked from previous step to present step. The FDI result of the snapshot method can be considered reliable independently with previous results and the FDI result of the filtering method is more reliable and detection time is a little longer. Therefore combining approach of two methods is proposed for increasing FDI performance in this paper. Three approaches that are the snapshot method, the filtering method and the combining method are compared to show the probability of correct FDI in simulations. The combining approach presents best result of FDI among them and shows the consistent accuracy irrespective of any changes in outdoor environment.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.823-826
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• 2000

Motion detection is an important step in computer vision and image processing. Traditional motion detection systems are classified into two categories, namely, feature based and gradient based. In feature based motion detection, features in consecutive frames are detected and matched. Gradient based methods assume that the intensity varies linearly and locally. The method, which we propose, is neither feature nor gradient based but uses the electric field theory. The pixels in an image are modeled as point charges and motion is detected by using the variations between the two electric fields produced by the charges corresponding to the two images.

• 한국통신학회논문지
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• 제37권8A호
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• pp.639-647
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• 2012

비전 기반의 차량 검출 기술은 자동 주행 보조 시스템에 있어서 가장 중요한 기술 중의 하나이다. 하지만 자동차 외형의 다양성 및 주변 환경의 변화로 인하여 정확하고 신뢰성 있는 차량 검출 시스템의 개발은 여전히 해결해야 될 문제로 남아 있다. 일반적으로 차량 검출 시스템은 두 단계로 구분할 수 있다. 차량 후보 영역을 검출하는 가설 생성(Hypothesis Generation(HG)) 단계와 가설 생성 단계에서 검출된 영역을 검증하는 가설 검증(Hypothesis Verification(HV)) 단계이다. 차량 검출은 HV 단계에서 최종적으로 검증 및 결정되기 때문에, HV 단계의 성능에 의하여 차량 검출의 성능이 결정되게 된다. 따라서, 본 논문에서는 축소 다변수 다항식 분류기(reduced multivariate polynomial pattern classifier(RM))를 HV 단계에 이용하여 고속 차량 검출 시스템을 구성하였다. 실험 결과 RM 분류기가 SVM 분류기 기반의 차량 검출 시스템보다 처리 속도 측면에서 월등한 성능을 보여 실시간 처리 기반의 차량 검출 시스템에 적합하다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.879-881
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• 2014

A method is proposed to achieve improved number plate detection for mobile devices by applying a two-step convolutional neural network (CNN) approach. Supervised CNN-verified car detection is processed first. In the second step, we apply the detected car regions to the second CNN-verifier for number plate detection. Since mobile devices are limited in computing power, we propose a fast method to detect number plates. We expect to use in the field of intelligent transportation systems (ITS).