AsphaltCrackNet: A novel architecture for classifying cracks in asphalt pavement
Publication date
2025-10-10
Document type
Forschungsartikel
Author
Organisational unit
Publisher
IEEE
Series or journal
IEEE access
ISSN
Periodical volume
13
First page
177160
Last page
177174
Peer-reviewed
✅
Part of the university bibliography
✅
Funding(s)
Language
English
Keyword
Convolutional neural network
Asphalt
Crack
Pavement
Deep learning
Attention
Abstract
Public roads and highways span hundreds of thousands of kilometers, and asphalt is the most widely used material for road construction. However, increasing traffic loads make it prone to wear and cracking, requiring efficient, scalable monitoring and maintenance strategies. To this extent, we propose AsphaltCrackNet, a novel lightweight deep learning architecture for real-time asphalt crack classification. Based on an enhanced U-Net architecture, it integrates multi-scale feature extraction, cross-attention modules, and attention gates to increase performance and interpretability. Evaluated using a five-fold cross-validation, AsphaltCrackNet achieves an accuracy of 99.18%, outperforming previous approaches. In addition, we visualize the classification decision, demonstrating that the models’ decisions are focused on image areas containing asphalt cracks. Lastly, we highlight the suitability of our lightweight architecture for deployment in embedded systems on vehicles or drones, with applications extending to both civilian and military infrastructure monitoring.
Description
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Version
Published version
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Metadata only access