Automatic detection of typical defects in reinforced concrete bridges via YOLOv5

Bridges play a crucial role in road networks, and ensuring their safety and preservation is of utmost importance for both management companies and the scientific community. Throughout their lifespan, bridges are exposed to various factors that increase their vulnerability, including aging, harsh environmental conditions, and natural hazards, all of which can potentially lead to structural failures. Following the collapse of the Polcevera Viaduct in Italy, the Ministry of Transportation proposed a comprehensive safety evaluation procedure to be implemented nationwide, aiming to develop a methodology for assessing critical cases and implementing risk mitigation measures. This process involves several stages to assign a risk class that considers different sources of hazards. Among these phases, periodic on-site surveys to identify defects and signs of degradation are required. However, several challenges arise, such as the time and cost associated with inspections, the subjectivity involved in visually identifying defects, and the need for qualified personnel. To address these issues, traditional techniques can be enhanced by leveraging digital innovations, which seek to create new and reliable tools that support road management companies in safeguarding their infrastructure assets. In this regard, deep learning-based object detectors offer promising possibilities. Specifically, automatic recognition of defects and damages on existing bridge elements can be achieved using single-stage detectors like YOLOv5. In this study, we explored the application of this technique by creating a database of typical defects and involving domain experts to label these defects. Subsequently, YOLOv5 was trained, tested, and validated, demonstrating favorable effectiveness and accuracy of the proposed methodology. This research opens new opportunities and highlights the potential of artificial intelligence in automatically detecting defects on bridges.