Artificial Intelligence System Combining with Infrared Thermography and Visible Image for Abnormal Temperature Detection and Floor Material Identification
Abstract
Thermographic imaging has gained significant use in recent years, particularly during the epidemic, including its application in architecture for damage detection on archaeological monuments through temperature analysis. The non-invasive nature of thermographic imaging, along with its ability to visualize temperature levels, allows for problem identification while preserving the building’s structure. The integration of artificial intelligence further enhances its potential applications. This study aims to propose an automated inspection system using a convolutional neural network (CNN) for analyzing abnormal floor blocks and its materials. A team of academicians with more than seven years of expertise in monument preservation gathered the imaging data for this investigation. They were in charge of collecting thermal imaging photos of floors at significant monuments and aiding in the identification of overheating data and floor tile types. This study will propose three types of CNN models for recognition: one for identifying floors in visible images, one for detecting abnormal temperatures in thermal images, and one for recognizing materials in visible images. The block with abnormal temperature radiations can be determined from the floor by analyzing elevated temperatures. Subsequently, analyzing materials in abnormal block can efficiently identify problematic materials. The identification accuracy rate of this study is as high as 99.16%. Compared to the efficiency of professionals identifying 100 images, this research increases efficiency by approximately 99.92%, which is an amazing improvement. These functions increase the practicality of restoration efforts, improve restoration quality and efficiency, and contribute to academic research on ancient monument preservation.
