Strategic SA-UNet: Integrating self-attention blocks into U-Net for efficient crack segmentation
Ryota Kobayashi, Munehiro Kimura, Ryosuke Harakawa, Norrima Mokhtar, Yang Zhou, Muhammad Amirul Aiman Asri, Raza Ali, Masahiro Iwahashi, Yuk Ming Tang, Yuk Ming Tang, Yuk Ming Tang
TL;DR
Strategic SA-UNet improves crack detection accuracy and efficiency for infrastructure monitoring by combining U-Net and self-attention blocks.
Contribution
Proposes Strategic SA-UNet, a novel crack segmentation model that reduces training time and computational cost while maintaining accuracy.
Findings
Strategic SA-UNet reduces training time by 83% compared to MixSegNet.
The model achieves high segmentation accuracy with 63% fewer FLOPs and 96% fewer model parameters.
It maintains high mIoU even with few training epochs, showing superior training efficiency.
Abstract
Accurate crack segmentation plays a crucial role in ensuring safety and mitigating disaster risks during road inspections and structural health monitoring. However, traditional image processing techniques often struggle with low detection accuracy and poor generalization performance due to the diverse morphology of cracks and the presence of background noise. To address these challenges, MixSegNet, a model that combines the strengths of convolutional neural networks (CNNs) and Transformers, has been proposed and demonstrated to achieve high segmentation performance. However, this enhanced precision comes at the cost of prolonged training cycles, which limits its applicability in operational environments such as infrastructure inspection, where new data must be acquired and processed continuously and rapidly. In this paper, to address this limitation, we propose Strategic SA-UNet…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning