Pavement crack detection based on a deep learning approach and visualisation by using GIS-Reference-Cited by-同舟云学术

Pavement crack detection based on a deep learning approach and visualisation by using GIS

Published:2023-02-09 Issue:1 Volume:24 Page:
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Dipankar Amit¹,Suman Sanjeev Kumar²

Affiliation:

1. Government Engineering College, Banka, Lakrikola, India

2. NIT Patna, Patna, India

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2023.2173754

Reference36 articles.

1. Mobile-based pavement system evaluation for Kuwait

2. Using PAVER 6.5.7 and GIS program for pavement maintenance management for selected roads in Kerbala city

3. Hybrid ABC-ANN for pavement surface distress detection and classification

4. A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

5. Cafiso S. et al. 2021. From manual to automatic pavement distress detection and classification . Ieeexplore.Ieee.Org. Available from: https://ieeexplore.ieee.org/abstract/document/8005711/ [Retrieved March 21 2022].

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Image Recognition of Pavement Cracks in Autonomous Driving Scenarios Based on Deep Learning;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19

2. Semantic segmentation for tooth cracks using improved DeepLabv3+ model;Heliyon;2024-02

3. Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC;IEEE Access;2024

4. A Proposed Intelligent Map Fusion for Pavement Crack Detection Using Structure from Motion;Lecture Notes in Networks and Systems;2024

5. Monitoring process stability in gas metal arc additive manufacturing based on arc sensing;Measurement;2023-10