• 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.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.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.

• 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.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1990.10a
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• pp.143-147
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• 1990

자동차는 주행시에 노면가진으로 인하여 진동을 받게 되는 데 이러한 진동 은 운전자의 승차감을 저하시킬 뿐만 아니라 화물과 차량 부품을 파손시키 는 원인이 되기도 한다. 노면가진력은 주로 현가장치를 통하여 차량에 전달 되어 차량의 진동을 유발하게 되므로 승차감을 향상시키려면 노면가진을 효 과적으로 차단할 수 있도록 현가장치를 설계하여야 한다. 이를 위하여 본 논 문에서는 차량을 현가장치의 형식에 따라 3가지 종류로 구분한 다음 각각을 12자유도로 모델링하여 노면이 불규칙(random)한 도로를 주행할 때의 승용 차의 진동을 해석하고, 현가스프링의 강성과 쇼크 압소바의 감쇠 값이 자체 수직 가속도 유효치(rms value)와 현가장치 유효행정(rms stroke length)에 미치는 영향을 분석하였다. 차량의 운동방정식은 현가장치의 비선형 특성을 고려하였기 때문에 미소 각 운동의 가정에도 불구하고 비선형 방정식이 된 다. 운동 방정식의 해는 4차 Runge-Kutta 적분법을 사용하여 수치해석으로 구하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.77-84
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• 2002

Recently, damage of pavement represented by crack is depened by the increase of traffic demand up to ten million and wight, and interest about the efficient management of pavement is being increased gradually according to the growth of maintenance expense of road surface. In this study, the possibility of application for acquisition of crack information was tested by appling DCRP and digital image processing technique and measuring crack on road surface precisely. Based on this, objective and efficient road surface measurement was planned and done. Measuring crack width, acquire result of comparative high accuracy. So, it is considered that it can be utilized as plan draft data for deterioration estimation and repair reinforcement work of pavement.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.145-154
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• 2014

The road network becomes more complex and extensive. Therefore, the inconveniences are caused in accordance with the time delay of the restoration of damaged roads, demands for excessive costs on information collection, and limitations on acquisition of damage information of the roads. Recently, road centric spatial information is gathered using mobile multi sensor system for road inventory. But expensive MMS(Mobile Mapping System) equipments require high maintenance costs from beginning and takes a lot of time in the data processing. So research is needed for continuous maintenance by collecting and displaying the damaged information on a digital map using low cost mobile camera system. In this research we aim to develop the techniques for mosaic with a regular ground sample distance using successive image from oblique camera on a vehicle. For doing this, mosaic image is generated by estimating the homography of high resolution oblique image, and the ground sample distance and appropriate overlap are analyzed using high resolution aerial oblique images which contain resolution target. Based on this we have proposed the appropriate overlap and exposure interval for mobile road inventory system.