Detection of anomalies in ceiling components from UGV-captured images

Abstract

This study proposes a method for the rapid detection of medium-scale ceiling damages, such as panel collapses or missing sections, by utilizing images captured by an Unmanned Ground Vehicle (UGV) and analyzing them with artificial intelligence. The UGV captures images of the ceiling, and the detection of ceiling damage is performed using PatchCore, an anomaly detection method that learns primarily from images of undamaged conditions. PatchCore enables the identification of abnormalities without requiring extensive datasets of damaged examples. Furthermore, we verify the potential to evaluate the extent of ceiling damage by calculating the total anomaly score based on the PatchCore-generated heatmap. To efficiently detect ceiling damage, the navigation algorithm of the UGV is also crucial. Since indoor environments of building structures are typically defined by wall surfaces, the navigation algorithm in which the UGV autonomously follows walls detected by LIDAR sensors is proposed. The effectiveness of the proposed method is validated through experiments conducted in a real building environment, where simulated damages are represented by open ceiling inspection hatches. These experiments confirm that the system can accurately detect and assess ceiling anomalies in practical settings.