E2S: A UAV-Based Levee Crack Segmentation Framework Using the Unsupervised Deblurring Technique

The accurate detection and monitoring of levee cracks is critical for maintaining the structural integrity and safety of flood protection infrastructure. Yet at present the application of using UAV to achieve an automatic, rapid detection of levee cracks is still limited and there is a lack of effective deblurring methods specifically tailored for UAV-based levee crack images. In this study, we present E2S, a novel two-stage framework specifically designed for UAV-based levee crack segmentation, which leverages an unsupervised deblurring technique to enhance image quality. In the first stage, we introduce an Improved CycleGAN model that mainly performs motion deblurring on UAV-captured images, effectively enhancing crack visibility and preserving crucial structural details. The enhanced images are then fed into the second stage, where an Attention U-Net is employed for precise crack segmentation. The experimental results demonstrate that the E2S framework significantly outperforms traditional supervised models, achieving an F1-score of 81.3% and a crack IoU of 71.84%, surpassing the best-performing baseline, Unet++. The findings confirm that the integration of unsupervised image enhancement can substantially benefit downstream segmentation tasks, providing a robust and scalable solution for automated levee crack monitoring.