Deep Learning–Based Automated Detection of Sewer Defects in CCTV Videos-Reference-Cited by-同舟云学术

Deep Learning–Based Automated Detection of Sewer Defects in CCTV Videos

Published:2020-01 Issue:1 Volume:34 Page:04019047
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Kumar Srinath Shiv¹^ORCID,Wang Mingzhu²,Abraham Dulcy M.³,Jahanshahi Mohammad R.⁴^ORCID,Iseley Tom⁵,Cheng Jack C. P.⁶^ORCID

Affiliation:

1. Ph.D. Candidate, Lyles School of Civil Engineering, Purdue Univ., HAMP 1240, 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). ORCID: .

2. Ph.D. Student, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Room 3586, Clear Water Bay, Kowloon, Hong Kong.

3. Professor, Lyles School of Civil Engineering, Purdue Univ., HAMP 1241, 550 Stadium Mall Dr., West Lafayette, IN 47907.

4. Assistant Professor, Lyles School of Civil Engineering, Purdue Univ., HAMP 4116, 550 Stadium Mall Dr., West Lafayette, IN 47907. ORCID: .

5. Professor, Dept. of Civil Engineering, Louisiana Tech Univ., Dan Reneau Dr., Ruston, LA 71270.

6. Associate Professor, Dept. of Civil and Environmental Engineering, Hong Kong Univ. of Science and Technology, Room 3586, Clear Water Bay, Kowloon, Hong Kong. ORCID: .

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.1943-5487.0000866

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