A Deep Learning Framework for Segmentation of Road Defects Using ResUNet-a-Reference-Cited by-同舟云学术

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A Deep Learning Framework for Segmentation of Road Defects Using ResUNet-a

Published:2024-06-26 Issue: Volume: Page:449-455
ISSN:
Container-title:Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments
language:
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Author:

Katsamenis Iason¹^ORCID,Kopsiaftis George¹^ORCID,Voulodimos Athanasios¹^ORCID,Rallis Ioannis¹^ORCID,Georgoulas Ioannis¹^ORCID,Zafeiropoulos Charalampos¹^ORCID,Doulamis Anastasios¹^ORCID

Affiliation:

1. National Technical University of Athens, Greece

Funder

RESEARCH ? CREATE ? IN- NOVATE

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652037.3663935

Reference52 articles.

1. An Integrated CNN-RNN Framework to Assess Road Crack

2. M. Z. Alom 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint:1802.06955 (2018).

3. Pothole Classification Model Using Edge Detection in Road Image

4. Semantic Segmentation with Boundary Neural Fields

5. Gunilla Borgefors. 1986. Distance transformations in digital images. Computer vision, graphics, and image processing 34, 3 (1986), 344–371.

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