• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.2
• /
• pp.104-113
• /
• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.5
• /
• pp.133-148
• /
• 2022

In this study, a road pavement crack deterioration model was developed for a pavement road sections of the Sejong-city. Data required for model development were acquired using a deep learning-based road asset monitoring system. Road pavement monitoring was conducted on the same sections in 2021 and 2022. The developed model was analyzed by dividing it into a method for estimating the annual average amount of deterioration and a method based on Bayesian Markov Mixture Hazard model. As a result of the analysis, it was found that an analysis results similar to the crack deterioration model developed based on the data acquired from the Automatic pavement investigation equipmen was derived. The results of this study are expected to be used as basic data by local governments to establish road management plans.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.3
• /
• pp.106-118
• /
• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.3
• /
• pp.95-105
• /
• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.4
• /
• pp.95-105
• /
• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.41 no.8
• /
• pp.619-624
• /
• 2013

To predict the probabilistic service life, statistical factors should be included to consider the uncertainty of parameters. Generally the probabilistic analysis is one of the common methods to account the uncertainty of parameters on the structural failure. In order to apply probabilistic analysis on the deterministic life analysis, it would be necessary to introduce Probability of Failure(PoF) and conduct risk assessment. In this work, we have studied probabilistic risk assessment of aircraft structures by using PoF approach. To achieve this goal, the Bayesian method was utilized to model PoF estimation since this method is known as the proper method to express the uncertainty of parameters. A series of proof tests were also conducted in order to verify the result of PoF estimation. The results from this efforts showed that the PoF estimation model can calculate quantitatively the value of PoF. Furthermore effectiveness of risk assessment approach for the aircraft structures was also demonstrated.

• International Journal of Highway Engineering
• /
• v.4 no.2 s.12
• /
• pp.9-18
• /
• 2002

The main distresses that influence pavement performance are rutting, fatigue cracking, and longitudinal roughness. Thus, it is important to analyze the factors that affect these three distresses, and to develop prediction models. In this paper, three distress prediction models were developed using DataPave program which stores data from a wide variety of pavement sections In the United States. Also, sensitivity studies were conducted to evaluate how the input variables impact on the distresses. The result of sensitivity study for the prediction model of rutting showed that asphalt content, air void, and optimum moisture content of subgrade were the major factors that affect rutting. The output of sensitivity study for the prediction model of fatigue cracking revealed that asphalt consistency, asphalt content, and air void were the most influential variables. The prediction model of longitudinal roughness indicated asphalt consistency, #200 passing percent of subgrade aggregate, and asphalt content were the factors that affect longitudinal roughness.

• 한국도로학회지:도로
• /
• v.8 no.1 s.27
• /
• pp.57-67
• /
• 2006

• International Journal of Highway Engineering
• /
• v.6 no.1 s.19
• /
• pp.13-23
• /
• 2004

This paper presents the results of a study that was performed to evaluate structural behaviors and related distress of asphalt pavements on concrete bridge deck based on the visual inspection and 3-dimensional finite element analysis. As a result, three most failure types were found such as permanent deformation, potholes, and fatigue crackings. In addition, the failure mechanisms of different types of concrete bridge deck were investigated. An increase in fatigue of asphalt pavements on concrete bridge deck was observed and confirmed by the results from the visual inspections. In consequence, the aging and stripping of asphalt surfacing materials are relatively dominant factors on fatigue rather than traffic loadings.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.593-596
• /
• 2020

많은 차량에 부착되어 있는 블랙박스를 활용하여 수집된 이미지 데이터들을 분석, 도로의 파손 상태를 체크하고 웹 서버에 표시하고 관련 기관에 신고합니다. 따라서 해당 도로를 이용하는 운전자들에게 도로의 상황을 알려주고 보수 조치가 빠르게 이루어 질 수 있도록 도와주는 서비스입니다.