Pixel-Level Crack Detection in Levee Systems: A Comparative Study

Levee systems require continuous monitoring and maintenance because of the risk of failure causing catastrophic flooding. This study explores several image segmentation methods based on fully convolutional neural networks to automatically detect cracks from images at the pixel level in the levee systems. Cracks are the leading cause of the failure of these flood water control structures. This research aims to use a levee crack dataset to train and evaluate the seven latest deep learning-based image segmentation models. With the training setup, the MultiResUnet model achieved the highest mean Intersection over Union (mIoU) of 87.777% on 10-Fold Cross-Validation and 87.766% on hold-out test data compared to other models.