• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.49-60
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• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.5
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• pp.90-99
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• 1996

To keep up with the regulation of OBD II(on board diagnostics II), many detection methods for engine misfire have been developed. Among them, the method of using the crankshaft speed fluctuation is the most noticeable in the point of view of lower cost and easier installation than any others. On the basis of the results obtained from the previous engine-dynamometer test, the integrating torque index (ITI) has been introduced. In this research, the instrumental and the interfacing systems to engine control unit(ECU) are developed for the vehicle test. Therefore, the vehicle and chassis-dynamometer test can be carried out in addition to the rough road test. From this test, the previousproposed method proved that it can be applied to the real vehicle.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.139.1-139.1
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• 2011

Electric vehicle use independent electricity of high voltage. if isolation of electricity is destructed, devices and people are considerably damaged. Therefore, detection of ground fault is necessary for electric vehicle. As the existing detection method of ground fault can not detect ground fault when isolation of both positive side and negative side of electricity is destructed, and change of voltage of electricity. This paper proposed detection method for ground fault of both two sides of electricity and change of voltage. The proposed method is verified by analysis of equivalent circuit.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.95-100
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• 2011

This paper proposes a new method of vehicle brake lights detection and recognition using an R-filtering. Firstly, the proposed method processes the R-filtering with the first input image and then with the second one in order to detect brake lights. Secondly, the method counts the number of red pixels and computes the mean value in each R-filtered image. The difference rates between the numbers of the red pixels and between the mean values of two images are defined in this paper. Through the analysis of the difference rates, it can recognize whether brake lights are turned on or off, and whether the vehicle ahead is being approached or not. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithm is quite successful.

• Korean Journal of Computational Design and Engineering
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• v.18 no.3
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• pp.155-166
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• 2013

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot's perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1685-1691
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• 2004

This paper describes the fault detection and failsafe logic to be used in an Electronic Stability Program(ESP). The aim of this paper is to prevent of erroneous controls in the ESP. Developed this paper introduces the fault detection logic and evaluation of residual signals. The failsafe logic consists of four redundant sub-models, which can be used for detecting the faults in various sensors (yaw rate, lateral acceleration, steering wheel angle). We present two mathematical residual generation methods : one is a method using the average value and the other is a method using the minimum value of the each residual. We verified a failsafe logic developed using vehicle test results also we compare vehicle model based simulation results with test vehicle results.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.128-135
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• 2019

This paper presents a deep learning-based object detection method for recognizing vehicles in images acquired through cameras installed on ceiling of underground parking lot. First, we present an image enhancement method, which improves vehicle detection performance under dark lighting environment. Second, we present a new CNN-based multiscale classifiers for detecting vehicles in images acquired through cameras with fisheye lens. Experiments show that the presented vehicle detector has better performance than the conventional ones.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.270-274
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• 2015

We suggest that HSV Color Space may be used to detect a vehicle detecting system at nighttime. It is essential that a licence plate should be extracted when a vehicle is under surveillance. To do so, a licence plate may be enlarged to certain size after the aimed vehicle is taken picture from a distance by using Pan-Tilt-Zoom Camera. Either Mean-Shift or Optical Flow Algorithm is generally used for the purpose of a vehicle detection and trace, even though those algorithms have tendency to have difficulty in detection and trace a vehicle at night. By utilizing the fact that a headlight or taillight of a vehicle stands out when an input image is converted in to HSV Color Space, we are able to achieve improvement on those algorithms for the vehicle detection and trace. In this paper, we have shown that at night, the suggested method is efficient enough to detect a vehicle 93.9% from the front and 97.7% from the back.