Pavement distress instance segmentation using deep neural networks and low-cost sensors-Reference-Cited by-同舟云学术

Pavement distress instance segmentation using deep neural networks and low-cost sensors

Published:2023-12-11 Issue:1 Volume:9 Page:
ISSN:2364-4176
Container-title:Innovative Infrastructure Solutions
language:en
Short-container-title:Innov. Infrastruct. Solut.

Author:

Mahdy Kamel^ORCID,Zekry Ahmed,Moussa Mohamed,Mohamed Ahmed,Mahdy Hassan,Elhabiby Mohamed

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology,Building and Construction,Civil and Structural Engineering,Environmental Engineering,Engineering (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s41062-023-01308-1.pdf

Reference48 articles.

1. Arya D, Maeda H, Ghosh SK, Toshniwal D, Mraz A, Kashiyama T, Sekimoto Y (2020) Transfer learning-based road damage detection for multiple countries

2. Arya D, Maeda H, Ghosh SK, Toshniwal D, Sekimoto Y (2022) RDD2022: a multi-national image dataset for automatic road damage detection

3. Augustaukas R, Lipnickas A (2019) Pixel-Wise Road Pavement Defects Detection Using U-Net Deep Neural Network. In: 10th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS), vol 1, pp 468–471

4. Banharnsakun A (2017) Hybrid ABC-ANN for pavement surface distress detection and classification. Int J Mach Learn Cybern 8(2):699–710. https://doi.org/10.1007/s13042-015-0471-1

5. Chen Y, Fan H, Xu B, Yan Z, Kalantidis Y, Rohrbach M, Yan S, Feng J (2019b) Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: and others (ed) Proceedings of the IEEE/CVF international conference on computer vision, pp 3435–3444