A Multitask Fusion Network for Region-Level and Pixel-Level Pavement Distress Detection-Reference-Cited by-同舟云学术

A Multitask Fusion Network for Region-Level and Pixel-Level Pavement Distress Detection

Published:2024-03 Issue:1 Volume:150 Page:
ISSN:2573-5438
Container-title:Journal of Transportation Engineering, Part B: Pavements
language:en
Short-container-title:J. Transp. Eng., Part B: Pavements

Author:

Zhong Jingtao¹^ORCID,Zhang Miaomiao²,Ma Yuetan³,Xiao Rui⁴,Cheng Guantao¹^ORCID,Huang Baoshan⁵^ORCID

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, Knoxville, TN 37996. ORCID: .

2. Postdoctoral Fellow, Dept. of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.

3. Research Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, Knoxville, TN 37996.

4. Postdoctoral Fellow, Dept. of Civil and Environmental engineering, Univ. of California, Los Angeles, Los Angeles, CA 90095.

5. Edwin G. Burdette Professor, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, Knoxville, TN 37996 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPEODX.PVENG-1433

Reference45 articles.

1. Deep learning-based road damage detection and classification for multiple countries;Arya D.;Autom. Constr.,2021

2. Encoder–decoder network for pixel‐level road crack detection in black‐box images

3. Review of Pavement Defect Detection Methods

4. A deep-learned embedding technique for categorical features encoding;Dahouda M.;IEEE Access,2021

5. Pavement distress detection and classification based on YOLO network

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