Novel System for Rapid Investigation and Damage Detection in Cultural Heritage Conservation Based on Deep Learning-Reference-Cited by-同舟云学术

Novel System for Rapid Investigation and Damage Detection in Cultural Heritage Conservation Based on Deep Learning

Published:2019-09 Issue:3 Volume:25 Page:04019020
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Wang Niannian¹,Zhao Xuefeng²,Wang Linan³,Zou Zheng¹

Affiliation:

1. Doctoral Candidate, School of Civil Engineering, Dalian Univ. of Technology, No. 2 Linggong Rd., Ganjingzi District, Dalian 116024, China.

2. Professor, School of Civil Engineering, Dalian Univ. of Technology, No. 2 Linggong Rd., Ganjingzi District, Dalian 116024, China (corresponding author).

3. Engineer, Chinese Academy of Cultural Heritage, Institute of Architecture Conservation, No. 2 Gaoyuan St., Chaoyang District, Beijing 100029, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000499

Reference41 articles.

1. Andolina S. D. Pirrone G. Russo S. Sorce and A. Gentile. 2012. “Exploitation of mobile access to context-based information in cultural heritage fruition.” In Proc. 2012 7th Int. Conf. on Broadband Wireless Computing Communication and Applications 322–328. New York: IEEE.

2. Inertial Sensor Sample Rate Selection for Ride Quality Measures

3. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

4. Chatfield K. K. Simonyan A. Vedaldi and A. Zisserman. 2014. “Return of the devil in the details: Delving deep into convolutional nets.” In Proc. BMVC 2014. Guildford UK: BMVA.

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