Automatic Inspection of Main Cables of Suspension Bridge Based on UAV and Apparent Defect Identification with Small-sized Samples
To realize automatic and intelligent inspection of main cables of a suspension bridge,a route planning method for cable inspection using an unmanned aerial vehicle(UAV)and apparent defect identification with small-sized samples is proposed.First,UAV oblique photogrammetry is utilized to rapidly construct a three-dimensional model of the targeted suspension bridge,facilitating a proposed automatic route planning for UAV inspection of main cables.Subsequently,the Faster R-CNN neural network model is employed to identify apparent defects such as cracks,corrosion,and scratches from images of the main cables.Finally,an image fusion-based data augmentation method is used to improve the accuracy of defect detection with a small-sized sample dataset.During the training process of the Faster R-CNN neural network model,the average accuracy of the three types of defects(i.e.,cracks,corrosion,and scratches)in the test dataset increases with the increase of the number of training epochs and gradually stabilizes after the 15th epoch.After 100 training epochs,the average accuracy for the three types of defects in the test dataset reaches 0.723.Field main cable inspections were conducted on the Xiaolongwan Bridge,and the results indicate that automatic UAV route planning for the inspection of main cables based on the established three-dimensional model is feasible in practice.The Faster R-CNN network model can accurately identify cracks,corrosion,and scratches in the main cables.The proposed fusion-based data augmentation method can effectively enhance the defect identification accuracy from small-sized samples.
NETL
NSTL
万方数据
