Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map

Lei, Xu; Liu, Chenglong; Li, Li; Wang, Guiping

doi:10.1109/access.2020.2989028

Cited by 37 publications

(12 citation statements)

References 38 publications

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“…To overcome these limitations several techniques, as well as thresholding and edge detection, have been proposed in the literature [35]. Furthermore, several ML techniques have been applied in the literature, based on different types of features, such as Scale-Invariant Feature Transform (SIFT) [36], Histogram of Oriented Gradients (HOG) [37], and classification algorithms, including Support Vector Machine (SVM) [38] and Bayesian Classifier [39] In recent years, Deep Learning (DL) techniques have proven to be particularly effective in various scenarios [40][41]. Therefore, several researchers have applied DL techniques for the recognition and classification to road's damages detection and classification [42][43][44].…”

Section: Machine Learning and Data Analysis Methods: Background And O...mentioning

confidence: 99%

“…Furthermore, several ML techniques have been applied in the literature, based on different types of features, such as Scale-Invariant Feature Transform (SIFT) [36], Histogram of Oriented Gradients (HOG) [37], and classification algorithms, including Support Vector Machine (SVM) [38] and Bayesian Classifier [39] In recent years, Deep Learning (DL) techniques have proven to be particularly effective in various scenarios [40][41]. Therefore, several researchers have applied DL techniques for the recognition and classification to road's damages detection and classification [42][43][44]. In research literature, several applications in this areas of application, including Convolutional Neural Networks (CNNs) [45], Region-Based CNN (R-CNN) [46], and Faster R-CNN [47] were presented, confirming the applicability and the research interest open the way for further research.…”

Section: Machine Learning and Data Analysis Methods: Background And O...mentioning

confidence: 99%

See 1 more Smart Citation

Automatic damage detection of bridge joints and road pavements by artificial neural networks ANNs

Gagliardi¹,

Bella

Sansonetti³

et al. 2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

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Monitoring the actual conditions of critical infrastructure assets is a priority for administrators to guarantee high-standard in terms of structural stability, and operational safety and to prevent damages and deterioration. Nowadays, most protocols for assessing road pavement conditions, including viaducts and bridges, are based on visual inspections conducted by specialized operators and, rarely, the local application of ground-based technologies such as IoT Wireless Sensors, laser scanners, Falling Weight Deflectometers, and accelerometers. However, the high costs of maintenance operations and on-site surveys, which involves the partial or total temporary closure of the infrastructure for the duration of the tests, still limit the implementation of these procedures at the network-scale level. Accordingly, the definition of innovative, automatic, and low-cost procedures for continuous monitoring operations, especially for road pavements and concrete bridges, is still an open challenge. In this context, recent developments in Artificial Neural Networks (ANNs) have opened new possibilities in the automatic recognition and geo-localization of damages affecting critical transportation assets, such as bridges and viaducts. This research presents an experimental application for the automatic detection of deteriorated bridge-deck expansion joints and the classification of pavement distresses based on Artificial Neural Networks models. More specifically, the methodology is founded on the latest generation of ANNs algorithms, among which “YOLO v5”. For this purpose, an experimental evaluation is conducted by the acquisition of a large dataset of imageries of road damages and bridge-deck joints, collected by open-source datasets, and the processing by ANNs. Furthermore, a field experiment was conducted in order to collect several videos of bridges, selected as case-studies, by means of an instrumented vehicle, through a High-Definition resolution camera. The acquired images were post-processed and implemented for the training phases of the ANNs and the verification phases of the developed model. The application of the presented approach provides a reliable, affordable, and promising methodology for the automatic identification and localization of pavement distresses and bridge-deck joints damages, to be rapidly managed and decisively actioned by administrative authorities and asset owners, giving crucial information that could be implemented for the prioritisation of maintenance activities within Asset Management Systems. The findings of this study demonstrate that ANNs approaches and Deep Learning algorithms, can be applied to complement Non-Destructive Remote Sensing technologies (e.g., satellite radar interferometry, Laser Scanners), localizing automatically the pavement and infrastructure damages, paving the way for integrated approaches in the smart monitoring of infrastructure assets.

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Section: Machine Learning and Data Analysis Methods: Background And O...mentioning

confidence: 99%

Section: Machine Learning and Data Analysis Methods: Background And O...mentioning

confidence: 99%

Automatic damage detection of bridge joints and road pavements by artificial neural networks ANNs

Gagliardi¹,

Bella

Sansonetti³

et al. 2022

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

View full text Add to dashboard Cite

show abstract

“…Pavement roughness can be measured using a cost-effective and sufficiently accurate RGB-D sensor [36]. Multiple deep-learning models have been applied to detect road-damage conditions; for example, the You Only Look Once version 3 (YOLO v3) deep-learning model has been used to identify and classify road defects on street-view imageries [37,38]. However, little research has been conducted on pavement-marking defects.…”

Section: Pavement-marking Defect Assessmentmentioning

confidence: 99%

Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale

et al. 2022

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Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management.

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“…Wang [32] CNN + PCA 1 Block Eisenbach [12] CNN 6 Block Zhang [33] CNN 1 Image Gopalakrishnan [34] CNN 1 Image Lau [35] U-Net N/A Block Escalona [35] U-Net N/A Block Some [36] CrackIt 1 Image Du [7] YOLO 7 Image Maeda [37] SSD 6 Image Lei [38] YOLOv3 8 Image Pereira [39] U-Net N/A Image of pavement crack in corresponding scale grids so the image only keeps the grids with cracks similar to a crack network as an output. Eisenbach et.…”

Section: Referencementioning

confidence: 99%

Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines

Sarmiento¹

2021

Preprint

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Road transport infrastructure is critical for safe, fast, economical, and reliable mobility within the whole country that is conducive to a productive society. However, roads tend to deteriorate over time due to natural causes in the environment and repeated traffic loads. Pavement Distress (PD) detection is essential in monitoring the current conditions of the public roads to enable targeted rehabilitation and preventive maintenance. Nonetheless, distress detection surveys are still done via manual inspection for developing countries such as the Philippines. This study proposed the use of deep learning for two ways of recording pavement distresses from 2D RGB images -detection and segmentation. YOLOv4 is used for pavement distress detection while DeepLabv3 is employed for pavement distress segmentation on a small dataset of pavement images in the Philippines. This study aims to provide a basis to potentially spark solutions in building a cheap, scalable, and automated end-to-end solution for PD detection in the country.

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Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map

Cited by 37 publications

References 38 publications

Automatic damage detection of bridge joints and road pavements by artificial neural networks ANNs

Automatic damage detection of bridge joints and road pavements by artificial neural networks ANNs

Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale

Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines

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