Timely and accurate inspection of concrete bridges is critical to ensuring structural integrity and public safety. Traditional visual inspections conducted by human inspectors are labour-intensive, inconsistent, and often limited in their ability to access all structural components, particularly in hazardous or inaccessible areas. Image-based inspection techniques have emerged as a safer and more efficient alternative, and recent advancements in deep learning have significantly enhanced their diagnostic capabilities. This systematic review critically evaluates 77 studies that applied deep learning approaches to the detection and classification of surface defects in concrete bridges using 2D images. Relevant publications were retrieved from major scientific databases, screened for eligibility, and analyzed in terms of model type, training strategies, and evaluation metrics. The reviewed works encompass a wide spectrum of algorithms—spanning classification, object detection, and image segmentation models—highlighting their architectural features, strengths, and trade-offs in terms of accuracy, computational complexity, and real-time applicability. Key findings reveal that transfer learning, data augmentation, and careful dataset composition are pivotal in improving model performance. Moreover, the review identifies emerging research trajectories, such as integrating deep learning with Building Information Modeling (BIM), leveraging edge computing for real-time monitoring, and developing rich annotated datasets to enhance model generalizability. By mapping the current state of knowledge and outlining future research directions, this study provides a foundational reference for researchers and practitioners aiming to deploy deep learning technologies in bridge inspection and infrastructure monitoring.
Shakeri et al. (2025) studied this question.