• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1556-1567
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• 2009

The death rate of pedestrian in car accidents in Korea is 2.5 times higher than the average of OECD countries'. If a system that can detect pedestrians and send alarm to drivers is built and reduces the rate, it is worth developing such a pedestrian detection system (PDS). Since the accident rate in which pedestrians are involved is higher at nighttime than in daytime, the adoption of nighttime PDS is being standardized by big auto companies. However, they are usually using night visions or multiple sensors, which are usually expensive. In this paper we suggest a method for nighttime PDS using single wide dynamic range (WDR) monochrome camera in visible spectrum band. In our experiments, pedestrians were accurately detected if only most edges of pedestrians could be obtained.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.815-825
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• 2017

Visibility of lane marking is impaired at night or in the rain, which thereby threatens traffic safety. Recently, various studies and technologies have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment productivity, improvement of lane marking visibility by applying phosphorescent material mixed paint. Cost-benefit analysis was performed with considering various benefit items that can be expected. About 45% of traffic accidents would be prevented by improving lane marking visibility. Additionally, accident reduction benefit and traffic congestion reduction benefit were calculated as much as 246 billion KRW per year and 12 billion KRW per year, respectively, by reducing repaint cycle due to enhanced durability. 45 billion KRW per year is expected to reduced with improved lane detection performance of autonomous vehicle. Meanwhile, total increased cost when introducing phosphorescent material mixed paint to 91,195km of nationwide road is identified as 1922 billion KRW per year. However, economic feasibility could not be secured with 0.16 of cost-benefit ratio when applied to the road network as a whole. In case of "Accident Hot Spot" analyzing section window (400m), one or more fatality or two or more injured (one or more injured in case of less than 2 lanes per direction) per year were caused by pavement marking related accident, economic feasibility was secured. In detail, 3.91 of cost-benefit ratio is estimated with comparison of the installation cost for 5,697 of accident hot spot and accident reduction benefit. Some limitations and future research agenda have also been discussed.