Crack detection, classification, and segmentation on road pavement material using multi-scale feature aggregation and transformer-based attention mechanisms
This paper introduces a novel approach to pavement material crack detection, classification, and segmentation using advanced deep learning techniques, including multi-scale feature aggrega- tion and transformer-based attention mechanisms. The proposed methodology significantly enhances the model’s a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/116173/1/constrmater-04-00036.pdf http://irep.iium.edu.my/116173/ https://www.mdpi.com/journal/constrmater |
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| Summary: | This paper introduces a novel approach to pavement material crack detection, classification, and segmentation using advanced deep learning techniques, including multi-scale feature aggrega- tion and transformer-based attention mechanisms. The proposed methodology significantly enhances the model’s ability to handle varying crack sizes, shapes, and complex pavement textures. Trained on a dataset of 10,000 images, the model achieved substantial performance improvements across all tasks after integrating transformer-based attention. Detection precision increased from 88.7% to 94.3%, and IoU improved from 78.8% to 93.2%. In classification, precision rose from 88.3% to 94.8%, and recall improved from 86.8% to 94.2%. For segmentation, the Dice Coefficient increased from 80.3% to 94.7%, and IoU for segmentation advanced from 74.2% to 92.3%. These results underscore the model’s robustness and accuracy in identifying pavement cracks in challenging real-world scenarios. This framework not only advances automated pavement maintenance but also provides a foundation for future research focused on optimizing real-time processing and extending the model’s applicability to more diverse pavement conditions. |
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