Damage detection of the ceiling elements based on Efficient GAN

Abstract

In this research, a damage detection method for the ceilings of buildings is proposed, utilizing Anomaly Detection by Efficient GAN, which enables anomaly detection based on images taken under normal conditions. The effectiveness of this method has been verified through empirical experiments conducted on the ceilings of actual buildings. The proposed method determines the presence of damage when the Anomaly Score exceeds a threshold established based on the Anomaly Score from normal conditions. Additionally, as the method of obtaining images of the target areas of buildings significantly influences the convenience of the damage detection method, both the use of UAVs and UGVs have been considered. The results of the investigation confirm that using UGVs is more convenient indoors due to the ease of autonomous navigation. This research demonstrates that UGVs offer higher practicality for indoor applications, thereby enhancing the efficiency of the damage detection process. The findings suggest that the proposed method, combined with the appropriate image acquisition means, provides a robust solution for the automated detection of ceiling damage in buildings.