• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.46-50
• /
• 1997

Acoustic signals from the vehicle muffler has various kinds of noises. For control of noise from the vehicle muffler, the major part of noise to be contorted is that correlated with the revolution of the vehicle engine. For this reason the most efficient method for noise control is to use the extracted acoustic signals correlated with revolution as a controlled factor. Therebefore in this paper we developed and proofed an algorithm for efficient amplitude detection and phase detection related to the engine operating revolution from the vehicle muffler noise by orthogonality.

• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.2
• /
• pp.125-133
• /
• 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.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.5
• /
• pp.392-399
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.1
• /
• pp.71-80
• /
• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.2
• /
• pp.349-354
• /
• 2015

This paper is about utilizing magnetic sensor to measure magnetic signal and analyze the form of magnetic signal for vehicle detection. For magnetic sensor, MR sensor from Honeywell company was used, and Helmholtz coil of which 3 axis' length is 1.2 m was manufactured to check the capability of the sensor and estimate its ability to detect the magnetic field. Vehicle detection was performed in following steps: installing sensor in road lane and non-road lane; estimating magnetic field when the vehicle is run by the driver; and estimating magnetic field of 7 different vehicles with different sizes. Also, sensor was installed at SUV and small-sized vehicle's park and non-park area to analyze the form of magnetic field. Lastly, the form of magnetic field made by different parts of the vehicle was analyzed. Based on the analysis, the form of magnetic field's magnetic peak value was bigger for road lane than non-road lane, complicated form was useful to distinguish the road lane above the installed sensor and the location of the running car, and the types of vehicle could be sorted because the variance of the magnetic field was bigger for bigger size of the vehicle. Also, it was confirmed that the forms of vehicle in parts-by-parts estimates.

• Journal of Korea Multimedia Society
• /
• v.16 no.6
• /
• pp.764-781
• /
• 2013

In this paper, we propose a highly precise vehicle detection method with low false alarm using billboard sweep stereo matching and multi-stage hypothesis generation. First, we capture stereo images from cameras established in front of the vehicle and obtain the disparity map in which the regions of ground plane or background are removed using billboard sweep stereo matching algorithm. And then, we perform the vehicle detection and tracking on the labeled disparity map. The vehicle detection and tracking consists of three steps. In the learning step, the SVM(support vector machine) classifier is obtained using the features extracted from the gabor filter. The second step is the vehicle detection which performs the sobel edge detection in the image of the left camera and extracts candidates of the vehicle using edge image and billboard sweep stereo disparity map. The final step is the vehicle tracking using template matching in the next frame. Removal process of the tracking regions improves the system performance in the candidate region of the vehicle on the succeeding frames.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.260-262
• /
• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• Journal of Korean Port Research
• /
• v.14 no.3
• /
• pp.303-312
• /
• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are lane angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.959-962
• /
• 1998

In this paper, we propose an efficient vehicle detection and classification algorithm for an electronic toll collection, which is based on shadow robust vehicle presence test. In order to improve the performance of vehicle presence test, we use correlation coefficients between wavelet transformed input and reference images, which takes advanage of textural similarity. We compare the performance of the vehicle presence test with those of some conventional approaches that use variance of frame difference. Experimental results from field test show that the proposed vehicl detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

• Smart Structures and Systems
• /
• v.2 no.3
• /
• pp.275-293
• /
• 2006

For health monitoring purpose usually the structure is instrumented with a large scale and multichannel measurement system. In case of highway bridges, operating vehicle could be utilized to reduce the number of measuring devices. First this paper presents a static damage detection algorithm of using operating vehicle load. The technique has been validated by finite element simulation and simple laboratory test. Next the paper presents an approach of using this technique to field application. Here operating vehicle load data has been used by instrumenting the bridge at single location. This approach gives an upper hand to other sophisticated global damage detection methods since it has the potential of reducing the measuring points and devices. It also avoids the application of artificial loading and interruption of any traffic flow.