A UAV Infrared Thermography-Based Framework for Preliminary Screening and Management of Suspected Facade Debonding Regions

Abstract

Facade debonding may lead to falling components and pose safety risks in dense urban environments, but infrared thermal responses may also arise from non-defect facade components and environmental conditions. Conventional facade inspection methods are often labor-intensive, hazardous, and difficult to integrate into digital maintenance workflows. To support safer and more efficient facade inspection and maintenance information management, this study develops an engineering-oriented inspection and management framework that integrates unmanned aerial vehicle infrared thermography, intelligent defect recognition, visual result verification, and defect information management. A UAV-based infrared data acquisition scheme was established, and a self-constructed dataset containing 1035 thermal images was developed for the detection of suspected facade debonding regions and common thermal interference sources, including windows, air-conditioning units, and signage. A lightweight detection model was embedded as the recognition engine of the framework to balance detection reliability and deployment efficiency under practical inspection conditions. Experimental results show that the proposed method achieved an mAP@0.5 of 87.8%, with 1.64 million parameters and 4.2 GFLOPs, indicating its potential for rapid preliminary facade screening under the tested computing configuration. Beyond model evaluation, an application platform was developed to support infrared image and video input, automatic detection, result visualization, statistical analysis, and defect record storage. The proposed framework demonstrates the potential of combining UAV infrared inspection and digital management tools for preliminary facade screening and inspection documentation, providing supporting information for subsequent engineering review and maintenance planning.