An end-to-end computer vision system based on deep learning for pavement distress detection and quantification

Author:

Cano-Ortiz SaúlORCID,Lloret Iglesias Lara,Martinez Ruiz del Árbol Pablo,Lastra-González PedroORCID,Castro-Fresno DanielORCID

Funder

Spain Ministry of Science and Innovation

Publisher

Elsevier BV

Reference46 articles.

1. Recent computer vision applications for pavement distress and condition assessment;El Hakea;Autom. Constr.,2023

2. A novel approach for pavement distress detection and quantification using RGB-D camera and deep learning algorithm;Lin;Constr. Build. Mater.,2023

3. A critical review and comparative study on image segmentation-based techniques for pavement crack detection;Kheradmandi;Constr. Build. Mater.,2022

4. Machine learning algorithms for monitoring pavement performance;Cano-Ortiz;Autom. Constr.,2022

5. Deep machine learning approach to develop a new asphalt pavement condition index;Majidifard;Constr. Build. Mater.,2020

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