Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure

Dai, Fei; Rashidi, Abbas; Brilakis, Ioannis; Vela, Patricio A.

doi:10.1061/(asce)co.1943-7862.0000565

Cited by 128 publications

(56 citation statements)

References 28 publications

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“…They are passive (i.e., do not emanate any sorts of energy and only use the natural light in the environment; [13][14][15]) and capable of providing semi-real-time information at relatively low-cost. A single inexpensive camera could be used to recognize actions of multiple pieces of equipment and minimize the rd International Symposium on Automation and Robotics in Construction (ISARC 2016) need for sophisticated on-board sensors for each piece of equipment [16].…”

Section: Computer Vision-based Activity Recognition Of Construction Ementioning

confidence: 99%

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Cheng¹,

Rashidi²,

Davenport³

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Action recognition and tracking of construction heavy equipment is the first step for benchmarking and analyzing the performance of individual machines and evaluating the productivity of a jobsite as a whole. Aside from direct observations, the current approaches for automatically recognizing and tracking various actions of construction heavy equipment includes: 1) using active sensors such as RFID tags, GPS and accelerometers or 2) computer vision-based activity analysis (processing images or videos).In this paper, we present a novel audio-based approach for activity recognition of construction heavy equipment. Construction machines often produce distinct sound patterns while performing certain activities and it is possible to extract useful information by recording and processing those audio files at construction jobsites. The proposed audiobased framework begins with recording generated sound patterns of construction equipment using commercially available audio recorders. The recorded signal is then fed into a signal enhancement algorithm to reduce background noise commonly found at construction jobsites. The modified audio signal is then converted into a time-frequency representation using the Short-Time Fourier Transform (STFT). A Support Vector Machine (SVM) is then trained to differentiate between the acoustic patterns of the various activities of each machine. The processed audio signal is then finally divided and classified into various activities using a window filtering approach and by setting proper thresholds. We implemented the presented audio-based system at several jobsites as case studies and the results illustrate the efficiency of the system in automatically recognizing various actions of construction heavy equipment.

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Section: Computer Vision-based Activity Recognition Of Construction Ementioning

confidence: 99%

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Cheng¹,

Rashidi²,

Davenport³

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…To test what we get from the combination of APR and PhotoScan, the use of the Laser scanning technology has allowed to reconstruct a 3D model of the Villa San Giovanni bridge by point cloud, comparing in terms of precision, time and cost modeling between the two image based and range based methodologies (Dai et al, 2013). For the scanning of the work, we used a Riegl LMS-Z420i laser scanner.…”

Section: Comparison With Tlsmentioning

confidence: 99%

Uav and Computer Vision, Detection of Infrastructure Losses and 3d Modeling

Barrile

Bilotta

Nunnari

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The degradation of buildings, or rather the decline of their initial performances following external agents both natural (cold-thaw, earthquake, salt, etc.) and artificial (industrial field, urban setting, etc.), in the years lead to the necessity of developing Non-Destructive Testing (NDT) intended to give useful information for an explanation of a potential deterioration without damaging the state of buildings. An accurate examination of damages, of the repeat of cracks in condition of similar stress, indicate the existence of principles that control the creation of these events. There is no doubt that a precise visual analysis is at the bottom of a correct evaluation of the building. This paper deals with the creation of 3D models based on the capture of digital images, through autopilot flight UAV, for civil buildings situated on the area of Reggio Calabria. The following elaboration is done thanks to the use of commercial software, based on specific algorithms of the Structure from Motion (SfM) technique. SfM represents an important progress in the aerial and terrestrial survey field obtaining results, in terms of time and quality, comparable to those achievable through more traditional data capture methodologies.

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“…Some researchers (Baltsavias, 1999;Dai & Lu, 2010;Dai et al, 2013;Dai et al, 2014) reviewed and evaluated benefits of employing SfM techniques for construction surveying and their related applications. However, adopting SfM technique is not ubiquitous for some 3D reconstruction scenarios due to some systematic errors.…”

Section: Issue Concerning the Side-view Sfm Datamentioning

confidence: 99%

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Meesuk¹

2017

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Although all care is taken to ensure the integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons Attribution-Non Commercial 4.0 International License, http://creativecommons.org/licenses/by-nc/4.0/Published by: CRC Press/Balkema PO Box 11320, 2301 EH Leiden, the Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com -www.taylorandfrancis.com to my dear family SummaryModelling urban flood dynamics requires detailed knowledge of a number of complex processes. Numerical simulation of flood propagation requires proper understanding of all processes involved. Also, special care has to be taken to adequately represent the urban topography, taking into account typical urban features like underpasses and hidden alleyways. Incorrect input data will affect the numerical model results, which may lead to inadequate flood-protection measures or even catastrophic flooding situations. This thesis explores how to include particular urban topographic features in urban flood modelling.Aerial Light Detection and Ranging (LiDAR) systems offer opportunities for achieving good quality topographic data, with less fieldwork on the ground. Even though aerial LiDAR data have long been used in many applications, conventional top-view LiDAR data can not quite capture some hidden urban features. However, recent improvements in Structure from Motion (SfM) techniques provide opportunities to achieve improved quality topographic data by using multiple viewpoints, including side-view data.This dissertation explores insights into the capabilities of assimilating SfM point cloud data by using multi-source views as input for enhancing 2D urban flood modelling. Side-view SfM point cloud data are collected and merged with conventional top-view LiDAR point cloud data to create novel Multi-Source Views (MSV) topographic data. The main objectives of this research are (i) to provide insight into the capabilities of using computer-based environments; (ii) to explore the benefits of using the new MSV data; (iii) to enhance 2D model schematizations; (iv) to compare simulated results using new MSV data and conventional top-view LiDAR data as input for urban flood models; and (v) to help developing flood-protection measures. Findings showed that simulation results using conventional top-view LiDAR-DSM as input show the least flood inundation areas and results contained many dry areas. This is because conventional LiDAR-DSM does not capture hidden urban features like underpasses, high trees, overarching structures, which behave as obstacles in 2D model schematics. When applying extended top-view LiDAR-DBM+ and the newly developed MSV-DEM as input, simulation results showed m...

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Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure

Cited by 128 publications

References 28 publications

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Uav and Computer Vision, Detection of Infrastructure Losses and 3d Modeling

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

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