Automated Bridge Inspection and Structural Damage Detection Using YOLOv5-s

An effective structural health monitoring system ensures that bridge structures are safe and durable. This paper introduces an innovative approach toward the automation of bridge inspection and the detection of structural damage using object detection with the YOLOv5-s algorithm. The model optimizes various types of structural defects such as cracks, corrosion, spalling, and deformation from image data. Based on high-speed performance and accuracy, the YOLOv5-s model is suited to handle larger datasets and will thus provide continuous, real-time monitoring of bridge conditions. With the model trained and tested on a very extensive dataset of bridge images, we show that it guarantees high accuracy in both defect detection and localization for this work. Our results suggest that an automated solution might save effort and reduce more time spent in the effort to achieve manual inspections. Future work involves improving the robustness of the model with regard to varied environments and adding more structural components.