Backpropagation Neural Network with Feature Sensitivity Analysis: Pothole Prediction Model for Flexible Pavements using Traffic and Climate Associated Factors-Reference-Cited by-同舟云学术

Backpropagation Neural Network with Feature Sensitivity Analysis: Pothole Prediction Model for Flexible Pavements using Traffic and Climate Associated Factors

Published:2020-08-05 Issue: Volume: Page:
ISSN:
Container-title:2020 the 3rd International Conference on Computing and Big Data
language:
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Author:

LAROZA SILVA DANTE¹,MARCELO DE JESUS KEVIN LAWRENCE¹

Affiliation:

1. Mapua University, Philippines

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3418688.3418699

Reference21 articles.

1. Song L. and Wang X. 2019. Faster region convolutional neural network for automated pavement distress detection. Road Materials and Pavement Design 1-19. DOI: https://doi.org/10.1080/14680629.2019.1614969 Song L. and Wang X. 2019. Faster region convolutional neural network for automated pavement distress detection. Road Materials and Pavement Design 1-19. DOI: https://doi.org/10.1080/14680629.2019.1614969

2. Cubero-Fernandez A. Rodriguez-Lozano F. J. Villatoro R. Olivares J. and Palomares J. M. 2017. Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing 2017 (1) 1-11. DOI: https://doi.org/10.1186/s13640-017-0187-0 Cubero-Fernandez A. Rodriguez-Lozano F. J. Villatoro R. Olivares J. and Palomares J. M. 2017. Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing 2017 (1) 1-11. DOI: https://doi.org/10.1186/s13640-017-0187-0

3. Computer vision based detection and localization of potholes in asphalt pavement images

4. Chen H. Yao M. and Gu Q. 2020. Pothole detection using location-aware convolutional neural networks. International Journal of Machine Learning and Cybernetics 11 (4) 899-911. DOI: https://doi.org/10.1007/s13042-020-01078-7 Chen H. Yao M. and Gu Q. 2020. Pothole detection using location-aware convolutional neural networks. International Journal of Machine Learning and Cybernetics 11 (4) 899-911. DOI: https://doi.org/10.1007/s13042-020-01078-7

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