Damage Detection Of Bridge Structure Based On Deep Learning

China is a big bridge country.Most of the bridge structures are reinforced concrete construction.With the increase of service life and the increase of urban traffic volume,most of the bridges put into use have more or less damage.The initial performance of the bridge damage is caused by cracks.The traditional bridge damage detection methods include manual detection method and bridge inspection method.There are many shortcomings.With the advancement of computer technology and the development of hardware facilities,the bridge structure damage detection technology based on machine vision has become the focus of everyone's attention.This paper studies the crack damage identification of bridges by introducing deep learning methods.Firstly,the research background and challenges of bridge structure damage detection are introduced.The current research status of deep learning development is expounded.The bridge structure damage detection and deep learning technology in artificial intelligence are combined to propose bridge crack based on deep learning method.Image detection and recognition technology.The design of the bridge crack data acquisition scheme and the manual collection of concrete crack image scheme are designed.Using convolutional neural network in deep learning to identify and classify crack images,a migration learning technique is proposed to solve the problem of training a large amount of training data required for convolutional neural networks.The data augmentation technology and the sliding window technology are introduced,and the collected crack data is divided into a training set and a test set.The network training is carried out separately,and the experimental results show that the method can classify the crack images better.Finally,the CIFAR-10 model is combined with the improved sliding window technology.Based on the crack extraction and localization algorithm of the bridge crack unit,the crack coordinates are finally selected.Compared with the traditional image recognition method,the method used in this paper is easier to operate in practical engineering,and the accuracy of the obtained results is higher.