• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.249-255
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• 2022

Potholes are an important clue to the structural defects of asphalt pavement and cause many casualties and property damage. Therefore, accurate pothole detection is an important task in road surface maintenance. Many machine learning technologies are being introduced for pothole detection, and data preprocessing is required to increase the efficiency of deep learning models. In this paper, we propose a preprocessing method that emphasizes important textures and shapes in pothole datasets. The proposed preprocessing method uses intensity transformation to reduce unnecessary elements of the road and emphasize the texture and shape of the pothole. In addition, the feature of the porthole is detected using Superpixel and Sobel edge detection. Through performance comparison between the proposed preprocessing method and the existing preprocessing method, it is shown that the proposed preprocessing method is a more effective method than the existing method in detecting potholes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.17-28
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• 2023

Potholes that occur on paved roads can have fatal consequences for vehicles traveling at high speeds and may even lead to fatalities. While manual detection of potholes using human labor is commonly used to prevent pothole-related accidents, it is economically and temporally inefficient due to the exposure of workers on the road and the difficulty in predicting potholes in certain categories. Therefore, completely preventing potholes is nearly impossible, and even preventing their formation is limited due to the influence of ground conditions closely related to road environments. Additionally, labeling work guided by experts is required for dataset construction. Thus, in this paper, we utilized the Mean Teacher technique, one of the semi-supervised learning-based knowledge distillation methods, to achieve robust performance in pothole image classification even with limited labeled data. We demonstrated this using performance metrics and GradCAM, showing that when using semi-supervised learning, 15 pre-trained CNN models achieved an average accuracy of 90.41%, with a minimum of 2% and a maximum of 9% performance difference compared to supervised learning.

• Korean Journal of Heritage: History & Science
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• v.46 no.3
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• pp.68-77
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• 2013

This report is based on the investigation of potholes which are formed by fluvial erosion. A pothole is called so because it is a hole that looks like a coffeepot. The results of previous studies are applied to the 'Jakgwaecheon Porthole' of this study. The study is focused on the dimension and morphology of the Pothole and investigates the effects of stream sediments, river flow, geological structural lines, etc. on the formation of potholes. As a result of measuring 61 potholes in this area, we recognized that the elliptical dish-shaped cross sections are dominant and inferred that their longitudinal direction on the plain is affected by the direction of the stream flow. Also, 'Jakgwaecheon Pothole' is very characterized in terms of scale and morphology. Furthermore, it is harmonious with the beautiful landscape, humanity, and historical values and it can be suggested that it is qualified to be registered as a geoheritage structure.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.177-178
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• 2014

• 한국도로학회지:도로
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• v.15 no.2
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• pp.43-51
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• 2013

• 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.

• Journal of The Geomorphological Association of Korea
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• v.18 no.4
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• pp.35-57
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• 2011

Minor erosional forms in the bedrock river, like potholes, are not just research subject for the professional geomorphologis. In addition, these features attract general public and make them understand the social contribution and importance of geomorphologic research activities. In this paper, the studies on bedrock minor forms in Korea was reviewed. For further researches, some of major erosional processes and minor forms in bedrock rivers were discussed in detail. Cavitation, plucking, hydro-wedging, and abrasion by passing sediment particles are the major processes to create the longitudinal or transverse minor forms like pothole, furrows, flutes, and runnels. Especially the definition of furrows and runnels are explained to prevent the confusion with pothole, weathering pits and grooves. To make a progress in research on bedrock minor forms the quantitative relationship between the variables should be studied. New techniques for scientific estimation of erosion rates and exposure ages of bedrock surfaces should be used in this field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.603-611
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• 2020

Potholes on the road directly affect drivers' safety, satisfaction, and vehicle damage. Thus, real-time detection and response are required. Increasing frequency of patrols allows for potholes to be detected and responded to quickly, but this takes much manpower, money, and time. In addition, potholes have different occurrence characteristics depending on the rain conditions, so it is necessary to consider the optimal frequency from an economic and road-service perspective. Therefore, a quantitative analysis was done on the effects of rainfall on the occurrence characteristics of potholes. Information on the persistence, impact of rainfall intensity, and weather information was collected over a long period. Based on the results, a risk-based, optimized, and changeable road-patrol strategy is presented. The analysis results show that the probability of pothole occurrence increases by 2.4 times in rainy weather. Furthermore, the impact continues for 3 days even after the rain stops. The probability of pothole occurrence increases by 0.46% per 1 mm of rainfall, and the occurrence characteristics react sensitively to even a small amount of rain of around 1 mm. It was concluded that road patrol is required at least once every three days for an effect-free period, while twice a day is needed for the "sphere of influence" period to achieve a 95% reliability level.ys for effect-free period, while twice a day for sphere of influence period to satisfy 95% reliability level.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.94-101
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• 2004

The purpose of this raper is to assess the benefits of frequency domain fatigue analysis and compare it with more conventional time domain techniques. The multi-body dynamic analysis, FE analysis and fatigue life prediction technique are applied for the frequency domain fatigue analysis. To obtain the dynamic load history used in the frequency domain fatigue analysis, the computer simulations running over typical road Profiles are carried out by utilizing vehicle dynamic model. The fatigue life estimation for the rear suspension system of small-sized passenger car is performed by using resonance durability analysis technique, and the estimation results are compared with the conventional quasi-static durability analysis results. For the pothole simulation, the percent changes, of the fatigue life between the two durability analysis techniques don't exceed 10%. But for the Belgian road simulation because of the resonance effect, the fatigue life using the resonance durability analysis technique are much smaller estimated than the quasi-static durability analysis results.