基于深度学习的钢桥螺栓关键点识别方法OA

To solve the problems of a large number of high-strength bolts,high risk of detachment,and low efficiency of manual inspection in steel structure bridges,a recognition method based on deep learning technology has been developed to identify the key points of high-strength bolts by locating 6 corner points and 1 center point of the nut(bolt head).Firstly,a dataset of high-strength bolts with large hexagonal heads for highway steel bridges was constructed through actual engineering photography and data augmentation methods.Then,a network model with the backbone of ResNet50 was designed and built.The annotated training set was converted into a heatmap and the model was trained.Subsequently,a steel bridge node bolt numbering rule and algorithm were proposed.Finally,the performance of the trained model was evaluated by the evaluation indicators of percentage of correct key points and accuracy.Key point localization experiments and robustness tests under different lightings were conducted on the model by newly collected bolt images.And the recognition accuracy of key points was verified through practical engineering.The research results indicate that the recognition rate of model bolts in both indoor experiments and actual engineering are 100％.The on-site recognition effect is better than the experimental results.This research result can provide reference for intelligent detection of high-strength bolt diseases in steel bridges.