Application of Deep Learning in Detecting Infrastructure Damage Using Digital Images

Abstract

Infrastructure damage detection is a fundamental component of structural maintenance and public safety. Conventional inspection methods generally rely on manual visual assessment, which is time-consuming, labor-intensive, and susceptible to human error. Recent advances in deep learning and computer vision have introduced image-based automated inspection systems capable of detecting, classifying, and assessing structural damage with high accuracy. This study aims to review and synthesize the application of deep learning techniques in digital image-based infrastructure damage detection. The study adopts a literature review approach by analyzing recent publications related to convolutional neural networks (CNN), transfer learning, semantic segmentation, transformer architectures, unmanned aerial vehicles (UAVs), and digital image correlation for structural health monitoring. The findings indicate that deep learning significantly improves detection performance for various infrastructure defects, including cracks, spalling, corrosion, and road surface deterioration. Advanced models integrating semantic segmentation and transformer-based architectures demonstrate superior accuracy in identifying damage under complex environmental conditions. Furthermore, UAV-assisted image acquisition enhances inspection efficiency while reducing operational costs and safety risks. Despite these advantages, several challenges remain, including limited annotated datasets, varying illumination conditions, model generalization, and computational requirements. Future research should emphasize multimodal data integration, explainable artificial intelligence, and real-time edge computing to improve practical implementation in infrastructure management. The adoption of deep learning-based inspection systems is expected to enhance preventive maintenance strategies and support sustainable infrastructure development.