A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

Chen, Xiao; Zhang, Xianfeng; Li, Jonathan; Miao, Ran; Zhou, Bo

doi:10.1109/tits.2022.3204334

Cited by 9 publications

(3 citation statements)

References 56 publications

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“…However, under repeated vehicle loads and harsh environmental conditions, road surface structures undergo aging and deterioration, eventually leading to road damage. This has a severe impact on road performance 1 . Therefore, the rapid and precise monitoring of road pavement damage and its distribution play a crucial role in extending the service life of highway roads.…”

Section: Background and Summarymentioning

confidence: 99%

An annotated street view image dataset for automated road damage detection

Ren,

Zhang,

Zhi

et al. 2024

Sci Data

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Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images.

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Section: Background and Summarymentioning

confidence: 99%

An annotated street view image dataset for automated road damage detection

Ren,

Zhang,

Zhi

et al. 2024

Sci Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…Xiao et al 39 constructed spectral index and gray co-occurrence matrix based on GF-2 remote sensing image data and used SVM classification method to realize the identification of road surface material types. Chen et al 40 designed a new method based on an improved recurrent neural network for identifying pavement with similar spectral features from Worldview-2 satellite images and realized automatic monitoring of asphalt pavement aging phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image

Wang,

Yang,

Xie

et al. 2024

J. Appl. Rem. Sens.

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For the evaluation of traffic infrastructure, asphalt pavement aging conditions are crucial. Due to the complexity of identifying and monitoring asphalt pavement aging conditions, many current studies tend to use satellite remote sensing methods. We conducted an extraction experiment on the aging status of asphalt pavement using on-site measured Pavement Surface Condition Index data and Gaofen-2 satellite (GF-2) high-resolution remote sensing images based on comprehensive references to previous research results. Based on our experimental results, the difference health index, ratio health index, and normalized difference health index can reflect asphalt pavement aging to varying degrees, but the correlation is relatively weak. The purpose of this paper is to propose a new asphalt pavement aging index (PAI), namely the PAI, based on sufficient experimental analysis. In addition to identifying asphalt pavement aging perfectly, PAI has a good ability to discriminate between road interference information, such as shadows and vehicles, after it has been verified. There is a significant linear relationship between its correlation coefficient R and pavement surface condition index, which is 0.894. The evaluation results of three sets of ground verification points obtained by applying PAI also demonstrate its practicality. Therefore, the combination of PAI and GF-2 high-resolution remote sensing images can be used to evaluate the aging status of asphalt pavements.

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“…The CNN can obtain spatial information of data smoothly, and the RNN can obtain the long-term time dependency information of data. However, a single CNN or RNN cannot consider space and time information at the same time, which means that it is one-sided for the CNN or RNN to solve the damage identification problem [38]. In addition, the CNN and RNN have their own shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

Liu

Mei

Wang

et al. 2023

JMSE

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As a new type of riser connecting offshore platforms and submarine pipelines, steel catenary risers (SCRs) are generally subject to waves and currents for a long time, thus it is significant to fully evaluate the SCR structure’s safety. Aiming at the damage identification of the SCR, the acceleration time series signals at multiple locations are taken as the damage characteristics. The damage characteristics include spatial information of the measurement point location and time information of the acquisition signal. Therefore, a convolutional neural network (CNN) is employed to obtain spatial information. Considering the variable period characteristics of the acceleration time series of the SCR, a gated recurrent unit (GRU) neural network is utilized to study these characteristics. However, neither a single CNN nor GRU model can simultaneously obtain temporal and spatial data information. Therefore, by combining a CNN with a GRU, the CNN-GRU model is established. Moreover, the hyperparameters of deep learning models have a significant influence on their performance. Therefore, particle swarm optimization (PSO) is applied to solve the hyperparameter optimization problem of the CNN-GRU. Thus, the PSO-CNN-GRU (PCG) model is established. Subsequently, an SCR damage identification method based on the PCG model is presented to predict the damage location and degree by SCR acceleration time series. By analyzing the SCR acceleration data, the prediction performances of the PCG model and the PSO optimization capacity are verified. The experimental results indicate that the identification result of the proposed PCG model is better than that of several existing models (CNN, GRU, and CNN-GRU).

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A New Method for Automated Monitoring of Road Pavement Aging Conditions Based on Recurrent Neural Network

Cited by 9 publications

References 56 publications

An annotated street view image dataset for automated road damage detection

An annotated street view image dataset for automated road damage detection

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image

A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

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