Statistical Analysis and Application Research on Surface Degradation of Historical buildings Based on ImageJ Machine Learning Technology
For Historical buildings,the surface deterioration of cultural relics is often one of the most important disease types due to the long-term effect of wind and rain erosion in nature.In the process of making protection and repair plans for such cultural relics,it is often necessary to make quantitative statistical analysis on the degree of surface deterioration as an important reference index to comprehensively judge its causes and provide a basis for making reasonable protection and repair measures.Usually,the surface deterioration signs of Historical buildings are mostly irregularly distributed,so it is difficult to accurately obtain the parameters such as the area,perimeter and spatial distribution of each deteriorated damaged point only by manual measurement,and it is difficult to provide accurate basic data support for the effective development of subsequent work.The machine learning technology based on ImageJ can effectively identify the images in a specific range based on the difference of spectral perception through certain image training technology,and can carry out many statistical analysis on the identification results,such as area and perimeter calculation.When this technology is applied to the accurate statistical analysis process of the signs of surface deterioration of the brick and stone heritage buildings,satisfactory results can be obtained.In this paper,the quantitative analysis of the damage of the external wall surface of a cultural relic building is taken as an example to explore the application path of this technology,and the analysis results and application scenarios are evaluated and prospected.
万方数据
维普
