Defect identification of Concrete Bridges Based on LAAD-YOLO11

In order to solve the problems of too many model parameters and too high computational complexity in concrete bridge damage detection, a lightweight bridge damage detection and recognition network based on YOLO11 model was designed. By introducing LADH detection head and lightweight Adown module, the detection accuracy is improved, while the number of model parameters and calculation cost are greatly reduced. The experimental results show that compared with the original YOLO11n model, the parameter number and calculation amount of this model are reduced by 30.8% and 33.3%, respectively, and a higher lightweight effect is achieved. The optimized model shows better efficiency and practicability in the task of bridge damage detection, and provides a more effective technical means for bridge health monitoring.