Crack Detection and Segmentation Using Deep Learning with 3D Reality Mesh Model for Quantitative Assessment and Integrated Visualization-Reference-Cited by-同舟云学术

Crack Detection and Segmentation Using Deep Learning with 3D Reality Mesh Model for Quantitative Assessment and Integrated Visualization

Published:2020-05 Issue:3 Volume:34 Page:04020010
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Kalfarisi Rony¹,Wu Zheng Yi²,Soh Ken³

Affiliation:

1. Associate Research Engineer, Bentley Systems, Incorporated, 27 Siemon Co Dr., Watertown, CT 06795.

2. Bentley Fellow, Bentley Systems, Incorporated, 27 Siemon Co Dr., Watertown, CT 06795 (corresponding author).

3. Senior Service Manager, Bentley Systems Singapore Pte. Ltd., 1 HarbourFront Place, #18-01 to 03, HarbourFront Tower One, Singapore 098633.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000890

Reference92 articles.

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3. Abe S. T. Okano and H. Sato. 1992. “A high speed image processor for detection of pavement cracks.” In Proc. IAPR Workshop on Machine Vision Applications 529–532. Tokyo: International Association for Pattern Recognition.

4. Abe S. T. Okano and H. Sato. 1993. “System integration of road crack evaluation system.” In Proc. SPIE: Int. Society Optical Engineering 38–48. Albuquerque New Mexico: Society of Photo Optical.

5. Stiffness reduction of cracked solids

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