On the use of YOLOv5 for detecting common defects on existing RC bridges

Monitoring and maintaining the health state of existing bridges is a time-consuming and critical task. To reduce the time and effort required for a first screening to prioritize risks, deep-learning-based object detectors can be used. In detail, automatic defect and damage recognition on existing elements of existing bridges can be performed using single-stage detectors, such as YOLOv5. To this end, a database of typical defects was gathered and labeled by domain experts and YOLOv5 was trained, tested, and validated. Results showed good effectiveness and accuracy of the proposed methodology, opening new scenarios and the potentialities of artificial intelligence for automatic defect detection on bridges.