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Strategic SA-UNet: Integrating self-attention blocks into U-Net for efficient crack segmentation
Ryota Kobayashi1, Munehiro Kimura1, Ryosuke Harakawa1
1Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, Nagaoka, Niigata, Japan.
Plos One
|March 6, 2026
Summary
Strategic SA-UNet offers efficient crack segmentation for infrastructure monitoring, achieving high accuracy with significantly reduced training time and computational costs compared to existing models.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Structural Engineering
Background:
- Accurate crack segmentation is vital for infrastructure safety and monitoring.
- Existing methods like MixSegNet achieve high accuracy but require extensive training.
- Limitations include poor generalization and high computational cost in operational settings.
Purpose of the Study:
- To develop an efficient crack segmentation network that reduces training time and computational cost.
- To maintain high segmentation accuracy comparable to state-of-the-art models.
- To enhance applicability in real-time infrastructure inspection.
Main Methods:
- Proposed Strategic SA-UNet, integrating a U-Net based CNN with a Self-Attention Block.
- The Self-Attention Block fuses local features with global context between encoder and decoder.
- Evaluated on publicly available crack datasets.
Main Results:
- Strategic SA-UNet achieved segmentation accuracy comparable to MixSegNet.
- Reduced training time by 83%, FLOPs by 63%, and model parameters by 96%.
- Demonstrated superior training efficiency with a high mean Intersection over Union (mIoU) in fewer epochs.
Conclusions:
- Strategic SA-UNet is an efficient and accurate crack segmentation model.
- The model is well-suited for real-time infrastructure inspection and structural monitoring.
- Offers a practical solution for continuous data processing in operational environments.
