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DCP-TransUNet: An Approach for Crack Segmentation on Roads.
Yunqing Liu1, Xu Du1, Weiguang Li2
1School of Electronic Information and Engineering, Changchun University of Science of Technology, Changchun 130118, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study presents DCP-TransUNet, a novel deep learning model for accurate cement pavement crack segmentation. The model enhances crack detection continuity and feature learning for improved road infrastructure safety.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Cracking is a major distress in cement pavements, impacting structural integrity and traffic safety.
- Accurate crack detection is crucial for effective pavement maintenance and management.
Purpose of the Study:
- To introduce DCP-TransUNet, a deep learning model for enhanced cement pavement crack segmentation.
- To improve the continuity and accuracy of crack extraction, especially under complex conditions.
Main Methods:
- Developed DCP-TransUNet with a hybrid encoder incorporating DSE-CNN and CLMA-Transformer blocks.
- Integrated a PPA bottleneck module to capture discriminative features for challenging crack imagery.
Main Results:
- Achieved high performance on a public dataset (mIoU 79.12%, Recall 87.96%, F1 87.06%, Precision 86.21%).
- Demonstrated strong results on a private dataset (mIoU 68.83%, Recall 74.42%, F1 77.57%, Precision 81.67%).
- Outperformed other models in crack segmentation accuracy and effectiveness.
Conclusions:
- DCP-TransUNet effectively segments cracks in cement pavements.
- The model's architecture enhances feature learning and interpretability for complex crack detection.
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