An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

Drouyer, Sebastien

doi:10.5201/ipol.2020.282

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…Its significant success in the field of image segmentation is attributed to two of its peculiarities: the classification occurs at the level of individual pixels, and, therefore, it is particularly adept at recognizing object edges and boundaries and the learning process is feasible with a reduced number of images. In this research, a modern version of the U-NET network has been implemented [50], which was published in 2020 and dedicated to the recognition of cracks in various types of materials. This version was obtained through training on a dataset obtained by merging multiple specific sub-datasets for cracks (Crack500 [51], DeepCrack [52], SDNet2018 [53], CrackForest [54]).…”

Section: Dnn Algorithmsmentioning

confidence: 99%

Deep neural networks for asphalt pavement distress detection and condition assessment

Gagliardi,

Giammorcaro,

Francesco

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Monitoring the current state of critical infrastructure assets is a crucial task for asset managers to ensure structural integrity, operational safety, prevent deterioration, and optimize maintenance scheduling. Currently, the most widely used approaches for evaluating road pavement conditions rely on visual inspections conducted by specialized operators and rarely integrate ground-based technologies such as laser scanners, Falling Weight Deflectometers, and accelerometers. However, the high costs associated with maintenance operations and on-site surveys, which often require temporary partial or total closure of the infrastructure during testing, still limit the widespread adoption of these procedures at a network-scale level. In this context, recent advancements in Deep Learning methodologies and the broader field of Artificial Intelligence have enabled researchers to explore the potential of automated recognition and geospatial localization of damages affecting critical transportation assets, including road pavements on bridges, viaducts, and tunnels. This study presents an experimental application based on Deep Neural Networks (DNN) for the automatic detection and assessment of pavement distress. The methodology builds upon DNN algorithms incorporating object detection and semantic segmentation capabilities, including "YOLO v7" and "U-Net", respectively. Firstly, a dataset comprising top-down images of road damages that affect road pavements, obtained from various open-source datasets, was collected to train the object detection task of YOLOv7. This process enables the automatic identification of five distinct classes of pavement distress. Furthermore, a method of assessment of pavement conditions, based on the outcomes of the DNNs, was proposed to determine the level of pavement deterioration and tested on a local road investigated in Rome, Italy. The application of the proposed approach offers a reliable and promising methodology for the automated identification, geospatial localization, and severity assessment of pavement damages, enabling rapid and decisive actions by administrative authorities. The obtained information is crucial for prioritizing maintenance activities. The study's findings demonstrate the potential of DNNs and Deep Learning algorithms to complement Non-Destructive Remote Sensing technologies (e.g., NDTs, Laser Scanners, InSAR) by automatically localizing pavement and infrastructure damages. This study paves the way for integrated approaches in the next generation of Pavement Management Systems (PMS)

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Section: Dnn Algorithmsmentioning

confidence: 99%

Deep neural networks for asphalt pavement distress detection and condition assessment

Gagliardi,

Giammorcaro,

Francesco

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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“…The GAPs v2 subset in [53] and SDNet2018 [119] with over 50k images for classification are steps in the right direction but it would also be beneficial to have annotated data for the segmentation and quantification tasks. A simple solution for this would be to collate several of the datasets such as proposed in [132]. However, there still is the issue with incorrect labels, which spans across several datasets in this space as outlined in subsubsection VI-A2.…”

Section: A Datasetsmentioning

confidence: 99%

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

König¹,

Jenkins²,

Mannion³

et al. 2022

Preprint

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Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

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“…Several research studies related to concrete crack image classification (e.g. [10,15,22] ) have deployed advanced computational resources and trained the used models using Graphics Processing Units (GPUs). All the experiments in this paper were conducted in Google Colaboratory (Colab) with the 12GB NVIDIA Tesla K80 GPU provided by the platform.…”

Section: Experimentation On the Proposed Dataset Using Dcnnsmentioning

confidence: 99%

Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks

2021

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Using Unmanned Aerial Systems (UASs) for bridge visual inspection automation necessitates the implementation of Deep Convolutional Neural Networks (DCNNs) to process efficiently the large amount of data collected by the UASs sensors. However, these networks require massive training datasets for the defects recognition and detection tasks. In an effort to expand existing concrete defects datasets, particularly concrete cracks in bridges, this paper proposes a public benchmark annotated image dataset containing over 6900 images of cracked and non cracked concrete bridges and culverts. The presented dataset includes some challenging surface conditions and covers concrete cracks with different sizes and patterns. The authors analyzed the proposed dataset using three state of the art DCNNs in Transfer Learning mode. The three models were used to classify the cracked and non cracked images and the best testing accuracy obtained reached 95.89%. The experimental results showcase the potential use of this dataset to train deep networks for concrete crack recognition in bridges. The dataset is publicly available at https://github.com/MCBDD-ZRE/Concrete-Bridge-Crack-Dataset- for academic purposes.

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An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

Cited by 13 publications

References 29 publications

Deep neural networks for asphalt pavement distress detection and condition assessment

Deep neural networks for asphalt pavement distress detection and condition assessment

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks

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