german asphalt pavement distress dataset


Towards Low-Cost Pavement Condition Health Monitoring …

location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

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Moisture sensitivity examination of asphalt mixtures using ...

Oct 09, 2016· Moisture damage in asphalt mixtures is a complicated mode of pavement distress that results in the loss of stiffness and structural strength of the asphalt pavement layers. This paper evaluated the moisture sensitivity of different aggregate–bitumen combinations through three different approaches: surface energy, peel adhesion and the ...

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Danial Mohammadzadeh S. - Civil Engineering (University ...

In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement

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Deep Machine Learning Approach to Develop a New Asphalt ...

42 based on a comprehensive dataset covering all pavement distress types from sections with 43 different conditions. Also, classification and quantifying the density of the distress did not take

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| AsphaltPro Magazine | Latest Pavement Technologies Can ...

The pavement management system (PMS) works efficiently when high-quality data is being input, as explained by Laura Inzerillo et.al., in the "Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress" in 2018. Reports like "A Review of Three-Dimensional Imaging Technologies for Pavement ...

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How to Improve Deep Learning based Pavement Distress ...

neural networks, the process of detecting pavement distress can be automated to a high degree. However, evaluations show that they perform relatively poor on road images, that are significantly different from training data. Therefore, we show, how the performance can be improved with a human in the loop. The basic idea is to enlarge the ...

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Background | Investigating the Relationship of As ...

147 Background The NCHRP 20-50(18) study objective was to determine the effect of in-place air voids on the performance of asphalt concrete (AC) pavements. The research focused on four primary distress types related to asphalt pavement performance: rutting, fatigue cracking, thermal cracking, and …

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| LRY's Blog

Oct 04, 2021· Dataset Tutorial about Road Damage Dataset above. We also created the tutorial of Road Damage Dataset. In this tutorial, we will show you: How to download Road Crack Dataset; The structure of the Dataset; The statistical information of the dataset; How to use trained models. Please check RoadDamageDatasetTutorial.ipynb. PID Pavement Image Dataset

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GitHub - hanshenChen/PID-Pavement-Image-Dataset

The dataset consists of images captured from two camera views of an identical pavement segment, i.e., a wide-view and a top-down view. The wide-view images were used to classify the distresses and to train the deep learning frameworks, while the top-down view images allowed calculation of distress density, which will be used in future studies ...

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Road Pavement Crack Detection Using Deep Learning with ...

Consider the most popular and public available datasets for road distress classification and detection tasks: 1) GAPs dataset [5]: includes total 1,969 gray valued are divided into 64×64 patches and each patch is labeled as a crack or not. The pictured surface material contains pavement of three different German federal roads.

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Deep Learning in Data-Driven Pavement Image Analysis and ...

Eisenbach et al. introduced the German asphalt pavement distress (GAPs) dataset as the first free and publicly available massive pavement distress images dataset suitable for training high-performance DCNNs. Almost all previous studies in this domain used their own datasets collected and annotated differently, and thus the performance of the ...

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German Asphalt Pavement Distress Dataset - GAPs ...

German Asphalt Pavement distress (GAPs) dataset introduced by Eisenbach et al. was evidently the first open source pavement distress image dataset appropriate for high-performance DCNNs training. The study involved 1,969 grayscale pavement images (1,418 for training, 500 for testing,

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Long-Term Pavement Performance (LTPP) - CKAN - Datasets

Apr 25, 2021· Long-term Pavement performance, construction, traffic, and environmental data for more than 2500 pavement sections in the United States and Canada. More than a dozen experimental designs address specially constructed and existing asphalt and concrete pavements, and maintenance and rehabilitation strategies.

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INTERNATIONAL JOURNAL OF ELECTRICAL AND ELECTRONIC …

Surveyors routinely measure pavement cracking as a part of road management activities with an effort to maintain pavements in a cost-effective manner [2]. Mainly, in civil engineering field, several . different types of pavement distress can develop in asphalt pavements. such as cracking, rutting, fretting, and loss of texture [1].

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Feature Pyramid and Hierarchical Boosting Network for ...

Jan 18, 2019· German Asphalt Pavement Distress (GAPs) dataset is presented in to address the issue of comparability in the pavement distress domain by providing a standardized high-quality dataset of large scale. The GAPs dataset includes a total of 1,969 gray valued images, with various classes of distress such as cracks, potholes, inlaid patches, et. al.

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Road Crack Detection And Segmentation For Autonomous …

pavement crack detection has been developed [Zhao,2010], whereas one more crack detection method has been proposed based on Gabor Filter [Salman,2013]. A comprehensive set of image processing algorithms for detection and characterization of road pavement surface crack distresses have been introduced in [Correia,2014].

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ERPUG2017 19-20 October Copenhagen, Denmark Abstracts

In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the

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Identifying Asphalt Pavement Distress Using UAV LiDAR ...

Sep 11, 2019· Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and …

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A Novel Approach to Data Augmentation for Pavement ...

We prove the effectiveness of our approach by evaluating the performance of three different CNNs for semantic segmentation on the German Pavement Distress dataset and on a novel asphalt dataset collected by us. Results show a remarkable increase in performance, especially with low cardinality classes, when CNNs are trained on the augmented ...

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Pavement Image Datasets: A New Benchmark Dataset to ...

Feb 24, 2020· Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi, and William G. Buttlar Transportation Research Record 2020 2674 : 2, 328-339

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Senganal Thirunavukkarasu - Associate Director, Data ...

This paper presents an analysis tool for predicting top-down cracking (TDC) of hot-mix asphalt (HMA) pavements. TDC is known to involve a complicated set of interactive mechanisms, perhaps more so than other HMA distresses. Such complexity makes it difficult to predict TDC reliably using conventional material models and analysis tools.

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(PDF) Feature Pyramid and Hierarchical Boosting Network ...

2) GAPs384: German Asphalt Pavement Distress (GAPs) dataset is presented in [20] to address the issue of com- parability in the pavement distress domain by providing a

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Dr. Kym Neaylon - Principal Engineering Advisor - Pavement ...

Kym is a chartered civil engineer with over 30 years specialist experience in bituminous road surfacings. He has enjoyed a successful background transitioning through an Australian State Road Agency, the Australian Road Research Board, an International professional services consultancy, to a position that enabled him to give back to the industry at the Centre for Pavement Engineering Education.

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