Identification of Defects in Pavement Images Using Deep Convolutional Neural Networks

Soloviev, А. N.; Sobol, B. V.; Vasiliev, P. V.

doi:10.1007/978-3-030-19894-7_46

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Soloviev et al [115], Li et al [116], Tong et al [117], and Fan et al [118] demonstrated the use of DCNNs to detect and recognize cracks as defects with quantifiable properties in applications for crack detection on pavement surfaces (e.g., crack length and size). Fan et al [119] proposed a CNN-based multi-label classifier by improving the positive-to-negative ratio of samples.…”

Section: Crack Detection Through Vision-based DLmentioning

confidence: 99%

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

381

158

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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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Section: Crack Detection Through Vision-based DLmentioning

confidence: 99%

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

381

158

View full text Add to dashboard Cite

show abstract

“…Soloviev et al [90] introduced a deep CNN model for identifying defects in road surface imagery. The model was implemented as a simplified and optimized version of the most popular networks that are currently completely connected.…”

Section: Flexible Pavement Constructionmentioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.

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The Study of Stratification of Multilayer Structures Based on Finite Element Modeling and Neural Network Technologies

Cherpakov

Vasiliev

Soloviev

et al. 2020

Springer Proceedings in Materials

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Identification of Defects in Pavement Images Using Deep Convolutional Neural Networks

Cited by 6 publications

References 19 publications

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Artificial Neural Networks for Flexible Pavement

The Study of Stratification of Multilayer Structures Based on Finite Element Modeling and Neural Network Technologies

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