Semantic Segmentation of Cracks on Masonry Surfaces Using Deep-Learning Techniques-Reference-Cited by-同舟云学术

Semantic Segmentation of Cracks on Masonry Surfaces Using Deep-Learning Techniques

Published:2024-05 Issue:2 Volume:29 Page:
ISSN:1084-0680
Container-title:Practice Periodical on Structural Design and Construction
language:en
Short-container-title:Pract. Period. Struct. Des. Constr.

Author:

Patel Sudhir Babu¹^ORCID,Bisht Pranjal²,Pathak Krishna Kant³^ORCID

Affiliation:

1. Research Scholar, Dept. of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India (corresponding author). ORCID: .

2. Dept. of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India.

3. Professor, Dept. of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Arts and Humanities (miscellaneous),Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/PPSCFX.SCENG-1410

Reference37 articles.

1. Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation

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

3. Bishop, C. M., and N. M. Nasrabadi. 2006. Vol. 4 of Pattern recognition and machine learning. New York: Springer.

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

5. Chaiyasarn K. W. Khan L. Ali M. Sharma D. Brackenbury and M. DeJong. 2018. “Crack detection in masonry structures using convolutional neural networks and support vector machines.” In Proc. Int. Symp. on Automation and Robotics in Construction. Paris: IAARC Publications.