IterLUNet: Deep Learning Architecture for Pixel-Wise Crack Detection in Levee Systems

Abstract

Deep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks in levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network to detect cracks from levee images at a pixel level automatically. We propose that the feature learning be strengthened using the decoder and bottleneck feature maps by concatenating them back to the encoder blocks. The addition reinforcement in the U-Net-like architecture results in a loop-like structure to exploit all the feature maps from encoders, bottlenecks, and decoders. The proposed architecture, Iterative Loop U-Net (IterLUNet), outperforms the state-of-the-art architectures on the image dataset of the levee system, achieving an increment of Intersection over Union (IoU) by 10.32% on average for a 10-Fold Cross-Validation (FCV) compared to the baseline U-Net model and 11.00%, 7.65%, and 7.43% with a range of latest models MultiResUnet, Attention U-Net, and Unet++ respectively. In addition, IterLUNet has at least 63% fewer parameters to be trained than the baseline model, thus, allowing less space consumption for pixel-wise crack detection in AI-based inspection of levee systems.