Triple-stage crack detection in stone masonry using YOLO-ensemble, MobileNetV2U-net, and spectral clustering

Abstract

Condition assessment of stone structures is crucial to maintain their durability. To improve the identification of stone cracks, a triple-stage framework for crack detection, segmentation, and decision-support clustering is proposed. The framework starts with an ensemble of state-of-the-art YOLO models to improve crack detection. The detected crack regions are then fed to an enhanced MobileNetV2U-Net for better crack localization. Thereafter, features are extracted from the detected and segmented stone crack regions, and the K-means and Spectral clustering are utilized to categorize crack patterns. Intensive experiments and detailed comparisons are performed to test the proposed approach. Finally, a user-friendly GUI is designed to simplify the complexity of the proposed framework. Results prove that the YOLO ensemble detector and MobileNetV2U-Net model exhibit the best performances based on statistical metrics. Moreover, it is proven that spectral clustering using five clusters applied to the detected-segmented crack patterns is the best-employed scenario.