Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

Chun, Chanjun; Ryu, Seung-Ki

doi:10.3390/s19245501

Cited by 70 publications

(35 citation statements)

References 17 publications

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“…(2019) proposed a hybrid machine learning approach to detect small cracks and corrosion of a pipeline. Chun and Ryu (2019) diagnosed the damage condition of road surfaces by fully convolutional neural networks. Alipour and Harris (2020) used web images and Google Street View imagery to evaluate the urban infrastructure defects.…”

Section: Related Workmentioning

confidence: 99%

Tunnel condition assessment via cloud model‐based random forests and self‐training approach

Zhu

Guo

et al. 2020

Computer aided Civil Eng

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To proactively assess the losses caused by the deterioration of metro tunnels during the operational period, a new method, the cloud model‐based random forests (CRFs), is proposed to discuss the inconsistencies induced by mapping the monitoring data into the health rate of the metro tunnels. On top of the CRF, a self‐training framework is introduced to improve the predictive accuracy and the stability of the CRF by adding more unlabeled data. The main contribution of this paper is proposing a novel CRF model along with a semisupervised approach to overcome the inapplicability of the random forests algorithm in an inconsistent small database, and to reduce the substantial time and cost for expert annotation. The results indicate that (1) the proposed CRF achieves higher accuracy and stability than random forests in predictions; (2) the CRF outperforms the other state‐of‐the‐art methods even in a small database; (3) the self‐training improved CRF keeps highly precise when the ratio of labeled to unlabeled data is no less than 1:11.4. In this study, the suggested ratio of labeled and unlabeled data is no lower than 1:5.7 to reduce the risk of wrongly forecasting a seriously damaged tunnel section as a slightly damaged tunnel section.

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Section: Related Workmentioning

confidence: 99%

Tunnel condition assessment via cloud model‐based random forests and self‐training approach

Zhu

Guo

et al. 2020

Computer aided Civil Eng

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“…The goal of the proposed algorithm is dependent on the developer's intention of which types of road damage to recognize. Jo et al designed an algorithm to recognize only potholes, and the algorithm by Chun et al was designed to detect both cracks and potholes [27], [28]. Eisenbach et al classified road damage into five types, such as cracks, potholes, and patches, according to the regulations by the Road and Transportation Research Association, and developed a deep neural network to distinguish the type [29].…”

Section: Image Data Of Road-surface Damage a Various Images Of Rmentioning

confidence: 99%

Lightweight Semantic Segmentation for Road-Surface Damage Recognition Based on Multiscale Learning

Shim

Cho

2020

IEEE Access

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With an aging society, the demand for personal mobility for disabled and aging people is increasing. As of 2017, the number of electric wheelchairs in Korea was 90,000 according to the domestic government statistics and has since increased continuously. However, people with disabilities and seniors are more likely to be involved in accidents while driving because their judgment and coordination are inferior to those of ordinary people. One of the factors that could lead to accidents is the interference in the vehiclesteering control owing to unbalanced road-surface conditions. In this paper, we introduce a lightweight semantic segmentation algorithm that can recognize the area with road-surface damage through images at high speed to prevent occurrence of such accidents. To test the algorithm, an experiment was conducted in which more than 1,500 training data and 150 validation data, including road-surface damage, were newly created. By using the data, we propose a new deep neural network composed of only encoder type, unlike the auto-encoding type consisting of encoder and decoder. To evaluate the performance of the proposed algorithm, we considered four metrics of the accuracy and two metrics of the speed. Unlike the conventional method, this deep neural network method shows improvement in all of the accuracy index, a 85.7% decrease in parameters, and an 6.1% increase in computational speed. The application of such a high-speed algorithm is expected to improve safety in personal transportation.

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“…We utilized U-Net, which is widely employed for segmentation tasks, as the basic framework for our segmentation model [ 18 ]. Although it was developed for biomedical image segmentation, it is also applied in the road traffic domain [ 19 ]. Additionally, U-Net is similar to a fully convolutional autoencoder [ 11 ].…”

Section: Segmentation Modelmentioning

confidence: 99%

Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation

Chun

Lee

Sun-Gil³

et al. 2020

Sensors

Self Cite

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Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.

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Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

Cited by 70 publications

References 17 publications

Tunnel condition assessment via cloud model‐based random forests and self‐training approach

Tunnel condition assessment via cloud model‐based random forests and self‐training approach

Lightweight Semantic Segmentation for Road-Surface Damage Recognition Based on Multiscale Learning

Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation

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