Comparison of U-Net and Fully Convolutional Networks (FCN) for concrete cracks detection using raw images under various conditions

Abstract

Crack detection in concrete buildings is crucial for assessing structural health, but it poses challenges due to complex backgrounds, real-time requirements, and high accuracy demands. Deep learning techniques, including U-Net and Fully Convolutional Networks (FCN), have shown promise in crack detection. However, they are sensitive to real-world environmental variations, impacting robustness and accuracy. This paper compares the performance of U-Net and FCN for concrete crack detection on bridges using raw images under various conditions. A dataset of 157 images (100 for training, 57 for testing) was used, and the models were evaluated based on Dice similarity coefficient and Jaccard index. FCN slightly outperformed U-Net in accuracy (94.88% vs. 94.21%), while U-Net had a slight advantage in validation (93.55% vs. 92.99%). These findings provide valuable insights for automated infrastructure maintenance and repair.