Integration of Probability Maps into Machine Learning Models for Enhanced Crack Segmentation in Concrete Bridges

Crack segmentation in concrete bridge structures is a critical task for ensuring safety and durability. This study focuses on evaluating and improving the performance of various deep learning models for crack segmentation, including U-Net, SegNet, ENet, HRNet, FastFCN, and DeepLab V3+. A novel approach is proposed which integrates a probability map generated by an ensemble of classification models as an additional input channel for segmentation models. This method demonstrated significant improvements in segmentation quality, increasing the IoU by up to 25.91% and F1 score by 15.39% compared with baseline models. These improvements were achieved through the use of additional spatial information provided by the probability map, enabling the models to detect cracks more precisely. Additionally, to evaluate the relevance of this approach, the results were compared with YOLO11x-seg, the latest and largest version for segmentation. These findings highlight that integrating auxiliary data channels into neural network architectures holds promise for enhancing segmentation accuracy in real-world engineering applications. The results of this study provide valuable insights for structural engineers and researchers working on automated crack detection, contributing to the development of reliable tools for structural health monitoring.