Disease detection on exterior surfaces of buildings using deep learning in China

Urban infrastructure, particularly in ageing cities, faces significant challenges in maintaining building aesthetics and structural integrity. Traditional methods for detecting diseases on building exteriors, such as manual inspections, are often inefficient, costly, and prone to errors, leading to incomplete assessments and delayed maintenance actions. This study explores the application of advanced deep learning techniques to accurately detect diseases on the exterior surfaces of buildings in urban environments, aiming to enhance detection efficiency and accuracy while providing a real-time monitoring solution that can be widely implemented in infrastructure health management. The research implemented a deep learning model that improves feature extraction and accuracy by integrating DenseNet blocks and Swin-Transformer prediction heads, trained and validated using a dataset of 289 high-resolution images collected from diverse urban environments in China. Data augmentation techniques improved the model's robustness against varying conditions. The proposed model achieved a high accuracy rate of 84.42%, a recall of 77.83%, and an F1 score of 0.81, with a detection speed of 55 frames per second. These metrics demonstrate the model's effectiveness in accurately identifying complex damage patterns, such as minute cracks, even within noisy urban environments, significantly outperforming traditional methods. This study highlights the potential of deep learning to transform urban maintenance strategies by offering a practical solution for the real-time detection of diseases on building exteriors, ultimately enhancing the efficiency and accuracy of urban infrastructure monitoring and contributing to improved maintenance practices and timely interventions.