A Convolutional Neural Network Based Pipeline for the Streamlining of the Masonry Quality Index Analysis

Abstract: YOLOv11, a CNN-based object detection and instance segmentation algorithm, is used to automatically capture Masonry Quality Index (MQI) parameters for existing masonry structures. Training is performed using a suitable dataset for detecting bricks, and its hyperparameters are adjusted systematically for optimal accuracy. A workflow is proposed in which models are trained on the “MCrack1300” dataset and evaluated using orthomosaics of an unreinforced masonry building. Optimal hyperparameters are determined iteratively, and their impact on minimizing loss is compared. The proposed model captures block size distributions and staggering ratios associated with the construction quality of masonry walls.