• Journal of Korean Society of Forest Science
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• v.97 no.6
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• pp.656-660
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• 2008

This study is carried out to minimize the damage to the forest road when locating strip roads in the future for stability of timberland after afforestation by assessing the factors that affect the damage on the forest road surface and making appropriate constructing standards. Major factors that influence damage to the strip road surface were location, longitudinal gradients, soil types, cross-section shape in order of influence on damage. it is considered that structural road factors like longitudinal gradients, road width, location factors such as construction location, slope gradients and road material like soil types were greatly related to occurrence of road surface damage. Damage occurrences in the forest road were severe at the valley, longitudinal gradients of over 24%, weathered granite soil, concave of road position, road width of over 3.0 m. stability was high at longitudinal gradients of 4~24%, road width of under 3.0 m, ridge of road position, straight slope, soil materials. The evaluation table of damage possibility on forest road was manufactured by discriminant analysis using Quantification theory(II). The results showed that the discriminant ratios was 79.4% and this table was available for forest manager.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.581-586
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• 2014

In general vibration test considers both harmonic vibration and random vibration, When developing the vehicle component. But the effect of harmonic vibration is larger in the parts around the vehicle engine, sole testing the harmonic vibration is considered. In this study, the fatigue damage of the linear system fixed around the engine is analyzed when the effect of random vibration is higher, harsher than the normal road surface condition. In condition the vehicle speed and the engine RPM are similar, the higher the harshness of the road surface condition is, the larger the fatigue damage level is. Therefore both random vibration and harmonic vibration must be considered in vibration test of components around the engine. Proposing the sine on random(SOR) vibration test that can exam considering both of vibrations, harmonic and random.

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

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 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
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• v.18 no.3
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• pp.106-118
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• 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
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• v.18 no.3
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• pp.95-105
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• 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.

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.71-79
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• 2019

By using convolutional neural networks (CNNs) based on semantic segmentation, road surface damage detection has being studied. In order to generate the CNN model, it is essential to collect the input and the corresponding labeled images. Unfortunately, such collecting pairs of the dataset requires a great deal of time and costs. In this paper, we proposed a road surface damage detection technique based on semi-supervised learning using pseudo labels to mitigate such problem. The model is updated by properly mixing labeled and unlabeled datasets, and compares the performance against existing model using only labeled dataset. As a subjective result, it was confirmed that the recall was slightly degraded, but the precision was considerably improved. In addition, the $F_1-score$ was also evaluated as a high value.

• Journal of Environmental Science International
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• v.31 no.4
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• pp.329-339
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• 2022

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22^nd December, 2020 and 29^th January, and 25^th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.266-271
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• 2009

Electronic equipments and a missile carried by heavy trucks are often subjected to vibration and shock excitation during their transportation. Electronic equipments are so vulnerable to vibration and shock input that it is necessary to know in advance the vibration level of the truck which cause the damage of equipments. Road tests of a heavy truck carrying a canister on different road conditions such as paved road, unpaved road, and washboard are performed and the effect of road conditions on the vibration characteristics are analyzed. Vibration levels were measured at various locations of the truck along the path through which vibration was transmitted. This study reveals that the velocity of the truck as well as the road surface conditions are main factors which affect the vibration levels of the truck. The power spectrum density of the measured vibration signal and the factors affecting the PSD are also analyzed in this paper.