Weakly-supervised segmentation with ensemble explainable AI: A comprehensive evaluation on crack detection

Surface cracks are crucial for structural health monitoring of various types of buildings. Despite substantial advancements in crack detection through deep neural networks, their reliance on pixel-level crack annotation escalates labeling costs and renders the labeling procedure time-intensive. Consequently, academics have suggested multiple Explainable Artificial Intelligence (XAI) methodologies to enhance the efficacy of pseudo-labeling. However, fractures' slender, continuous, and inconspicuous characteristics render current XAI approaches ineffective in adequately gathering feature information. This work examines the characteristics of many XAI strategies through extensive experimentation. It synthesizes the advantages of each strategy to mitigate the uncertainty error associated with a singular model in the fracture region. Moreover, we formulate and implement various integration strategies to mitigate and enhance the discrepancies across distinct XAI algorithms across two separate datasets. The experimental results indicate that the proposed method provides more accurate basic annotations for weakly supervised crack segmentation.