Research On Bridge Crack Detection Method Based On Collision-proof UAV Video

With the rapid development of China’s economy,the traffic volume is also increasing day by day.With the rapid development of China’s economy,the volume of transportation is increasing day by day,and bridges,as an important infrastructure for transportation,are under increasing pressure.With the passage of the bridge’s operating time,the collapse of the bridge accidents continue to occur,causing a lot of economic losses and casualties.In this context,bridge safety operation and maintenance and health monitoring are increasingly important.In view of the problems of low automation,crack recognition accuracy and efficiency of traditional bridge detection methods,the collision-proof UAV developed in this study is equipped with high-resolution cameras,supplemented by sensors such as laser ranging and high-precision positioning for bridge inspection image data Collected and used deep learning technology to identify,classify and classify bridge cracks to realize the automation and intelligence of bridge safety detection.The thesis first summarizes the existing bridge inspection data collection technology and analysis methods at home and abroad,and proposes a method for detecting cracks in the bottom of the bridge by using collision-proof drones to address the shortcomings of the existing methods.The design of collision-proof UAV platform,the selection of high-resolution camera,the development of collision-proof hood,etc.were explained,and the data collection route plan of collision-proof UAV bridge bottom was designed to complete the data collection work.Through data preprocessing,the bridge crack data set required for deep learning is produced.Then,based on deep learning,the area extraction and classification of bridge cracks were studied,and the YOLOv3-Inception network was designed according to the characteristics of bridge cracks.After experimental verification,the accuracy of the network on the bridge crack data set can reach 92.5%.The rate can reach 94.8%.Finally,in order to accurately calculate the crack size,the crack area extracted by depth learning is analyzed pixel by pixel based on Otsu adaptive threshold segmentation algorithm,and the area is divided into crack pixels and non crack pixels,and the size of bridge crack is analyzed by the total number of crack pixels and the maximum and minimum number of crack width pixels.Experiments show that the method of deep learning combined with digital image processing can detect bridge cracks quickly and accurately,and realize the automation and intelligence of bridge detection.