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Article

36 Citations

6,760 Views

22 Pages

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

Andri Riid,
Roland Lõuk,
Rene Pihlak,
Aleksei Tepljakov and
Kristina Vassiljeva

Appl. Sci.2019, 9(22), 4829;https://doi.org/10.3390/app9224829

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11 November 2019

The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the...

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Article

12 Citations

4,739 Views

27 Pages

Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks

Roland Lõuk,
Andri Riid,
René Pihlak and
Aleksei Tepljakov

Algorithms2020, 13(8), 198;https://doi.org/10.3390/a13080198

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14 August 2020

In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (...

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Article

13 Citations

11,243 Views

32 Pages

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

Rosa Meo,
Elena Roglia and
Andrea Bottino

Sensors2012, 12(12), 17504-17535;https://doi.org/10.3390/s121217504

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17 December 2012

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

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