A Novel Proactive Fault Tolerance Loss Function for Crack Segmentation

Bingchao Li, Zihao Li, Jianping Zong, Huaichao Wang, Nansha Li, Haifeng Li

Abstract: Optimizing the generalization performance of road surface crack models in practical applications represents a challenging task. Especially for thin and irregular cracks with random and expansive topologies, the loss functions used in current deep learning-based crack segmentation models are sensitive to single pixels, which tends to cause the model to overfit the training data, diminishing its generalization ability in real-world scenarios. Therefore, we take the loss function as a starting point and explore the introduction of proactive fault tolerance mechanisms into the training process of the crack segmentation model, which is called Proactive Fault Tolerance Loss (PFT Loss), to enhance the generalization capability of model in actual applications. Specifically, the PFT Loss function establishes correlations between the segmentation prediction pixels and the corresponding labeled pixels within the neighborhood window using Markov Random Fields (MRFs). The correlation is used as a reference for predicting relative shifts in segmented pixels. Proactive Fault Tolerance is performed on the loss between labeling and prediction to achieve a more natural and adaptive training method for crack segmentation. Full experiments are conducted on five public crack datasets and one self-constructed dataset. The experimental results indicate that the model trained with PFT Loss has better segmentation performance compared to other loss functions.