Semantic Deep Learning Integrated with RGB Feature-Based Rule Optimization for Facility Surface Corrosion Detection and Evaluation-Reference-Cited by-同舟云学术

Semantic Deep Learning Integrated with RGB Feature-Based Rule Optimization for Facility Surface Corrosion Detection and Evaluation

Published:2021-11 Issue:6 Volume:35 Page:04021018
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Rahman Atiqur¹,Wu Zheng Yi²,Kalfarisi Rony³

Affiliation:

1. Former Research Intern, Bentley Systems Inc., Watertown, CT; presently, Research Scientist, Facebook Inc., 1 Hacker Way #15, Menlo Park, CA 94025.

2. Bentley Fellow, Director of Applied Research, Bentley Systems Inc., 27 Siemon Company Dr., Watertown, CT 06795 (corresponding author).

3. Software Engineer II, Bentley Systems Singapore Pte. Ltd., 1 Harbour Front Place, Harbour Front Tower One, #18-01 to 03, 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.1943-5487.0000982

Reference49 articles.

1. AbdelRazig Y. A. 1999. “Construction quality assessment: A hybrid decision support model using image processing and neural learning for intelligent defects recognition.” Ph.D. dissertation Dept. of Civil Engineering Purdue Univ.

2. IMAGE PROCESSING TECHNIQUES APPLIED FOR PITTING CORROSION ANALYSIS

3. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

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

5. Bentley Systems. 2018. “InspectTech—Inspection maintenance and asset management for transportation infrastructure: Bridge tunnel road rail and transit.” Accessed May 20 2018. https://prod-bentleycdn.azureedge.net/-/media/files/documents/product-data-sheet/pds_inspecttech_ltr_en_lr.pdf?la=en&modified=20181030213411.

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