Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic
Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the...
Saved in:
| Main Authors: | Crespin Prudence Yabi, Godfree F. Gbehoun, Bio Chéissou Koto Tamou, Eric Alamou, Mohamed Gibigaye, Ehsan Noroozinejad Farsangi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/10/5/111 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images
by: Fahrizal, et al.
Published: (2025-06-01) -
Deep Metric Learning-Based Classification for Pavement Distress Images
by: Yuhui Li, et al.
Published: (2025-06-01) -
Constitutive modelling and numerical simulation of dynamic behaviour of asphalt-concrete pavement
by: A. ZBICIAK
Published: (2014-09-01) -
Machine Learning-Based Highway Pavement Performance Prediction in Xinjiang
by: Qi Yang, et al.
Published: (2025-07-01) -
A Review on the Technologies and Efficiency of Harvesting Energy from Pavements
by: Shijing Chen, et al.
Published: (2025-07-01)