A Detection Method for Pavement Cracks Combining Object Detection and Attention Mechanism-Reference-Cited by-同舟云学术

A Detection Method for Pavement Cracks Combining Object Detection and Attention Mechanism

Published:2022-11 Issue:11 Volume:23 Page:22179-22189
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Yao Hui¹^ORCID,Liu Yanhao¹,Li Xin¹,You Zhanping²^ORCID,Feng Yu³,Lu Weiwei⁴

Affiliation:

1. Beijing Key Laboratory of Traffic Engineering, Faculty of Architecture, Civil and Transportation Engineering, College of Metropolitan Transportation, Beijing University of Technology, Chaoyang, Beijing, China

2. Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, MI, USA

3. Hunan Expressway Group Company Ltd., Kaifu, Changsha, Hunan, China

4. Testing and Consulting Company Ltd., Changsha University of Science and Technology, Changsha, Hunan, China

Funder

U.S. National Science Foundation

Beijing Key Laboratory of Traffic Engineering

Hunan Expressway Group Company Ltd.

Department of Transportation of Hunan Province

National Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

https://ieeexplore.ieee.org/ielam/6979/9942712/9827988-aam.pdf

Reference63 articles.

1. Faster region convolutional neural network for automated pavement distress detection

2. Automated pavement crack detection and segmentation based on two‐step convolutional neural network

3. Pixel-level Crack Detection using U-Net

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

5. DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

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