Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies-Reference-Cited by-同舟云学术

Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

Published:2021-12 Issue:6 Volume:35 Page:04021092
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Jiang Yuhan¹^ORCID,Han Sisi²,Bai Yong³^ORCID

Affiliation:

1. Assistant Professor, Dept. of Construction and Operations Management, South Dakota State Univ., Brookings, SD 57007. ORCID: .

2. Graduate Student, Dept. of Civil, Construction and Environmental Engineering, Marquette Univ., P.O. Box 1881, Milwaukee, WI 53201-1881 (corresponding author).

3. McShane Chair and Professor, Dept. of Civil, Construction and Environmental Engineering, Marquette Univ., P.O. Box 1881, Milwaukee, WI 53201-1881. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CF.1943-5509.0001652

Reference53 articles.

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2. Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks

3. Augustaukas R. and A. Lipnickas. 2019. “Pixel-wise road pavement defects detection using U-net deep neural network.” In Proc. 2019 10th IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 468–471. New York: IEEE.

4. Autodesk. 2020. “3D view.” Accessed July 4 2020. https://help.autodesk.com/view/RECAP/ENU/?guid=Reality_Capture_View_and_Navigate_Point_Cloud_Color_Settings_3D_View_html.

5. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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