• Journal of Digital Contents Society
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• v.18 no.5
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• pp.989-994
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• 2017

In this paper, we propose a pothole detection system using 2D LiDAR and a pothole detection algorithm. Conventional pothole detection methods can be divided into vibration-based method, 3D reconstruction method, and vision-based method. Proposed pothole detection system uses two inexpensive 2D LiDARs and improves pothole detection performance. Pothole detection algorithm is divided into preprocessing for noise reduction, clustering and line extraction for visualization, and gradient function for pothole decision. By using gradient of distance data function, we check the existence of a pothole and measure the depth and width of the pothole. The pothole detection system is developed using two LiDARs, and the 3D pothole detection performance is shown by detecting a pothole with moving LiDAR system.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.8-19
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• 2016

Potholes are caused by the presence of water in the underlying soil structure, which weakens the road pavement by expansion and contraction of water at freezing and thawing temperatures. Recently, automatic pothole detection systems have been studied, such as vibration-based methods and laser scanning methods. However, the vibration-based methods have low detection accuracy and limited detection area. Moreover, the costs for laser scanning-based methods are significantly high. Thus, in this paper, we propose a new pothole detection system using a commercial black-box camera. Normally, the computing power of a commercial black-box camera is limited. Thus, the pothole detection algorithm should be designed to work with the embedded computing environment of a black-box camera. The designed pothole detection algorithm has been tested by implementing in a black-box camera. The experimental results are analyzed with specific evaluation metrics, such as sensitivity and precision. Our studies confirm that the proposed pothole detection system can be utilized to gather pothole information in real-time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.3
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• pp.603-610
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• 2017

Various type of pavement deterioration such as crack, bumpy, pothole is rapidly increasing according to the accelerated environmental changes like heavy rainfall, frequent snowing, difference temperature, etc. Accident related to pothole that cause fatal traffic accidents has been increased more than five times over the next five years starting from 2008. As direct or indirect damage by pothole which caused injuries and car damages increases every year, quicker and more efficient management measures are necessary. This study presents the algorithm for materials quantity take-off. The algorithm was suggested by correlation in pothole size and area. Suggested algorithm were confirmed the validity through the 15 field survey in capital area. According to the results of survey, usually the residual materials at which 5~7 kg was generated decreased to 1~2 kg. It showed that automatic pothole detection system is expected not only to reduce materials and resources, but also to contribute to quality improvements of pavement through more accurate material take-off from the situation of constructing rely on their own judgement.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.88-93
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• 2021

The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1035-1040
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• 2024

South Korea has seen an increased demand for road maintenance, since they experienced a rapid industrialization in 1960-70s. Between 2019 and the end of 2022, the total national expenditure on road maintenance steadily rose from KRW 3.4 trillion to KRW 4.5 trillion. Roads, responsible for about 80% of the nation's transportation, significantly affect ride quality, safety and maintenance costs. Among the different perspectives, this study focuses on the prevalence of potholes. Over 24,000 pothole instances are reported on highways in the past five years, which raises concerns due to various direct and indirect effects on road maintenance and safety issues. Various methods, including vision-based, vibration-based, and 3D reconstruction-based techniques, have been proposed for pothole detection and inspection. Vision-based methods effectively count and measure pothole shapes but which are sensitive to lighting conditions. Vibration-based methods offer cost-effectiveness, although it may not provide precise pothole shape information. 3D reconstruction-based methods deliver accurate shape measurements, while it comes with higher costs. To establish an effective road maintenance system, prioritization criteria for potholes is required to be established and applied. These criteria may vary across countries or regions. For example, in the United States, potholes are classified based on depth into Low (<25mm deep), Moderate (25 to 50mm deep), and High (>50mm deep). In conclusion, this research addresses this research challenge of road damage and potholes in South Korea by exploring various pothole classification standards and utilizing advanced technology to develop an efficient road maintenance system. The outcome would benefit improved road infrastructure management and enhanced safety.