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Author: Luana Lopes Amaral Loures
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Unpaved roads have a significant role in Canada's transportation and service activities, accounting for close to 60% of Canada's total public road networks. Furthermore, they connect agricultural, mining, recreational areas, and small communities to the nearby towns and businesses. An effective maintenance program for a network of unpaved roads requires a detailed assessment of the road surface's condition, and such assessment is usually made by visual inspections which can be time-consuming and error prone. The main part of these evaluations aims to identify distresses on the road surface, such as washboarding (corrugation), potholes, and rutting. Many research studies have developed methods to automate condition assessment of asphalt roads by combining machine learning algorithms and low-cost unmanned aerial vehicles (UAV), but the research on the automated assessment of unpaved roads is very limited. A system has been developed in this study to automate the assessment of unpaved roads by coupling computer vision methods, namely deep convolutional neural networks, and UAV-based imaging. This automated system could be used as an alternative method to reduce the need for human effort and possible manual errors, and therefore improve road maintenance programs in remote areas. The performance of the proposed method was evaluated using different test settings, and despite having some challenges, such as false positives, it showed promising outcomes that can contribute to the proposed purpose of this research. This proposed method has a potential for further improvement and the findings can be used as a basis for similar studies.
Author: Luana Lopes Amaral Loures
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Unpaved roads have a significant role in Canada's transportation and service activities, accounting for close to 60% of Canada's total public road networks. Furthermore, they connect agricultural, mining, recreational areas, and small communities to the nearby towns and businesses. An effective maintenance program for a network of unpaved roads requires a detailed assessment of the road surface's condition, and such assessment is usually made by visual inspections which can be time-consuming and error prone. The main part of these evaluations aims to identify distresses on the road surface, such as washboarding (corrugation), potholes, and rutting. Many research studies have developed methods to automate condition assessment of asphalt roads by combining machine learning algorithms and low-cost unmanned aerial vehicles (UAV), but the research on the automated assessment of unpaved roads is very limited. A system has been developed in this study to automate the assessment of unpaved roads by coupling computer vision methods, namely deep convolutional neural networks, and UAV-based imaging. This automated system could be used as an alternative method to reduce the need for human effort and possible manual errors, and therefore improve road maintenance programs in remote areas. The performance of the proposed method was evaluated using different test settings, and despite having some challenges, such as false positives, it showed promising outcomes that can contribute to the proposed purpose of this research. This proposed method has a potential for further improvement and the findings can be used as a basis for similar studies.
Author: Elham Eslami
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this dissertation introduces a Multi-Scale Convolution Neural Network (MCNN) for multi-class classification of pavement images. MCNN improves the classification performance by encoding contextual information through multi-scale input tiles. Then, an Attention-Based Multi-Scale CNN (A+MCNN) is proposed to further improve the classification results through a novel mid-fusion strategy for combining multi-scale features extracted from multi-scale input tiles. An attention module is designed as an adaptive fusion strategy to generate importance scores and integrate multi-scale features based on how informative they are to the classification task. Finally, Dual Attention CNN (DACNN) is introduced to improve the performance of multi-class classification using both intensity and range images collected with 3D laser imaging devices. DACNN integrates information in intensity and range images to enhance distinct features improving the objects classification in noisy images under various illumination conditions. The standard road condition assessment includes determining not only the type of defects but also the severity of detects. In this regard, a pavement crack segmentation algorithm, CrackSegmenter, is proposed to detect crack at pixel level. The CrackSegmenter leverages residual blocks, attention blocks, Atrous Spatial Pyramid Pooling (ASSP), and squeeze and excitation blocks to improve segmentation performance in pavement crack images.
Author: Assefa Melesse
Publisher: Elsevier
ISBN: 0443153426
Category : Science
Languages : en
Pages : 468
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Book Description
Remote Sensing of Soil and Land Surface Processes: Monitoring, Mapping, and Modeling couples artificial intelligence and remote sensing for mapping and modeling natural resources, thus expanding the applicability of AI and machine learning for soils and landscape studies and providing a hybridized approach that also increases the accuracy of image analysis. The book covers topics including digital soil mapping, satellite land surface imagery, assessment of land degradation, and deep learning networks and their applicability to land surface processes and natural hazards, including case studies and real life examples where appropriate. This book offers postgraduates, researchers and academics the latest techniques in remote sensing and geoinformation technologies to monitor soil and surface processes. - Introduces object-based concepts and applications, enhancing monitoring capabilities and increasing the accuracy of mapping - Couples artificial intelligence and remote sensing for mapping and modeling natural resources, expanding the applicability of AI and machine learning for soils and sediment studies - Includes the use of new sensors and their applications to soils and sediment characterization - Includes case studies from a variety of geographical areas
Author: Vinay K Chawla
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Book Description
Assessing pavement condition is extremely essential in any effort to reduce future economic losses and improve the structural reliability and resilience. Data resulting from pavement condition assessment are used as a record of infrastructure performance and as a major component to assess their functionality and reliability. However, pavement condition assessment is challenging because of the cost associated with assessment, safety issues, and the accessibility restrictions, especially after natural hazards. This research aims to develop an automated classification model to rapidly classify pavement distresses. High-resolution aerial images representing alligator and longitudinal cracks are collected for flexible pavements using Unmanned Aerial Vehicle (UAV) around East Carolina University (ECU) campus. The image classification model is developed using Convolutional Neural Network (CNN), a deep learning approach. The results of the developed model indicate an accuracy of 96.7% in classifying the two categories of pavement distress. The developed model was further tested on a set of test images yielding a prediction accuracy of 90%. The methodology behind the developed model will help to reduce the need for on-site presence, increase safety, and assist emergency response managers in deciding the safest route to take after hurricane events. Additionally, application of the model will enable transportation engineers in rapidly assessing the pavement damage, aid in making quick decisions for road rehabilitation and recovery and devise a restoration or repair plan.
Author: Sabina Shahnaz
Publisher:
ISBN:
Category : Gravel roads
Languages : en
Pages : 416
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Book Description
Author: Alessandro Matese
Publisher: MDPI
ISBN: 3039367544
Category : Science
Languages : en
Pages : 184
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Book Description
Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.
Author: Jaap Zevenbergen
Publisher: CRC Press
ISBN: 1498719619
Category : Nature
Languages : en
Pages : 298
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Book Description
Advances in Responsible Land Administration challenges conventional forms of land administration by introducing alternative approaches and provides the basis for a new land administration theory. A compilation of observations about responsible land administration in East Africa, it focuses on a new empirical foundation rather than preexisting ideal
Author: A. R. Jha, Ph.D.
Publisher: CRC Press
ISBN: 1315354012
Category : Computers
Languages : en
Pages : 190
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Book Description
This book provides a complete overview of the theory, design, and applications of unmanned aerial vehicles. It covers the basics, including definitions, attributes, manned vs. unmanned, design considerations, life cycle costs, architecture, components, air vehicle, payload, communications, data link, and ground control stations. Chapters cover types and civilian roles, sensors and characteristics, alternative power, communications and data links, conceptual design, human machine interface, sense and avoid systems, civil airspace issues and integration efforts, navigation, autonomous control, swarming, and future capabilities.
Author: Ken Skorseth
Publisher:
ISBN:
Category : Gravel roads
Languages : en
Pages : 112
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Book Description
The purpose of this manual is to provide clear and helpful information for maintaining gravel roads. Very little technical help is available to small agencies that are responsible for managing these roads. Gravel road maintenance has traditionally been "more of an art than a science" and very few formal standards exist. This manual contains guidelines to help answer the questions that arise concerning gravel road maintenance such as: What is enough surface crown? What is too much? What causes corrugation? The information is as nontechnical as possible without sacrificing clear guidelines and instructions on how to do the job right.
Author: Eduardo Toledo Santos
Publisher: Springer Nature
ISBN: 3030512959
Category : Technology & Engineering
Languages : en
Pages : 1332
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Book Description
This book gathers the latest advances, innovations, and applications in the field of information technology in civil and building engineering, presented at the 18th International Conference on Computing in Civil and Building Engineering (ICCCBE), São Paulo, Brazil, August 18-20, 2020. It covers highly diverse topics such as BIM, construction information modeling, knowledge management, GIS, GPS, laser scanning, sensors, monitoring, VR/AR, computer-aided construction, product and process modeling, big data and IoT, cooperative design, mobile computing, simulation, structural health monitoring, computer-aided structural control and analysis, ICT in geotechnical engineering, computational mechanics, asset management, maintenance, urban planning, facility management, and smart cities. Written by leading researchers and engineers, and selected by means of a rigorous international peer-review process, the contributions highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations.
