Intelligent assessment system of material deterioration in masonry tower based on improved image segmentation model

Abstract Accurate and timely data collection of material deterioration on the surfaces of architectural heritage is crucial for effective conservation and restoration. Traditional methods rely heavily on extensive field surveys and manual feature identification, which are significantly affected by objective conditions and subjective factors. While machine vision-based methods can help address these issues, the accuracy, intelligence, and systematic nature of material deterioration assessment for large-scale masonry towers with complex geometries still require significant improvement. This research focuses on the architectural heritage of masonry towers and proposes an intelligent assessment system that integrates an improved YOLOv8-seg machine vision image segmentation model with refined 3D reconstruction technology. By optimizing the YOLOv8-seg model, the system enhances the extraction capabilities of both detailed and global features of material deterioration in masonry towers. Furthermore, by complementing it with image processing methods for the global visualization of large-scale objects, this research constructs a comprehensive intelligent assessment process that includes "deterioration feature extraction—global visualization—quantitative and qualitative comprehensive assessment." Experimental results demonstrate that the intelligent assessment system significantly improves the performance of target feature extraction for material deterioration in masonry towers compared to existing methods. The improved model shows improvements of 3.39% and 4.55% in the key performance metrics of mAP50 and mAP50-95, respectively, over the baseline model. Additionally, the efficiency of global feature extraction and visualization of material deterioration increased by 66.36%, with an average recognition accuracy of 95.78%. Consequently, this system effectively overcomes the limitations and subjective influences of field surveys, enhancing the objectivity and efficiency of identifying and analyzing material deterioration in masonry towers, and providing invaluable data support for the subsequent preservation and restoration efforts.