Cross-Material Damage Detection and Analysis for Architectural Heritage Images

This study addresses the strategic requirements for cultural heritage preservation as specified, alongside the needs of high-quality urban-rural development. It highlights the inefficiency, subjectivity, and lack of intelligence in traditional manual detection methods used in architectural heritage preservation. Consequently, this research explores intelligent damage detection and quantitative analysis through image detection technology based on artificial intelligence. Firstly, a cross-material classification standard for architectural remnants is developed to facilitate data annotation in image detection techniques. Secondly, a dense object detection algorithm specifically designed for architectural images is proposed to address challenges such as boundary ambiguity and high-density damage in architectural heritage. This algorithm effectively facilitates intelligent detection and quantitative analysis of architectural heritage damage. On this basis, multiple datasets for architectural heritage damage detection are compiled and constructed on-site. Experimental results obtained from these datasets demonstrate that the proposed method surpasses comparative approaches across various metrics, including average precision, thus confirming its feasibility and effectiveness. Additionally, a software application for intelligent damage detection and quantitative analysis of architectural heritage images is developed, providing novel insights and support for the field of architectural heritage preservation.