Research On Identification And Classification Of Road Surface Damage Based On UAV

Highway traffic is an important way of national traffic,but with the increase of service life,and under the influence of various internal and external effects,the performance of the road surface will gradually decline until the occurrence of diseases affecting traffic.The deterioration of pavement performance is usually manifested by cracking,deformation,looseness and rutting.Among these damages,cracking usually occurs in the early stage of pavement performance deterioration,and the cracks generated by cracking are the most common form of pavement damage.Therefore,accurately and timely detection of road cracks and analysis of their characteristics and influence is the top priority of road maintenance work.The detection and identification of road damage has always adopted the method of combining visual inspection personnel and road inspection vehicle inspection,these methods are usually cumbersome,inefficient and time-consuming.Therefore,this paper uses the UAV with flexible operation,high efficiency and low cost as a new detection tool to detect road damage.In this paper,DJI MAVIC 2 PRO unmanned aerial vehicle is used to carry HASSELBLAD camera for road surface image acquisition,according to the industry standard and field investigation,a total of 10,000 pavement damage images with transverse cracks,longitudinal cracks,diagonal cracks and map crackings were collected and made into a new data set.Based on the deep learning method,a comprehensive detection model combining the automatic detection,location,crack classification,recognition,segmentation and extraction of pavement damage and the automatic calculation of damage parameters was proposed.Firstly,the pavement damage detection and identification model is used to detect and locate the damaged cracks,the class confidence of each cracks was obtained after classification.Then the crack information is transmitted to the pavement damage segmentation and extraction network of the next level,and the selected pavement damage cracks are segmented and extracted accurately.Finally,the length,width and area of each crack in the damaged image are calculated according to the segmentation results,and the image positioning information collected by the UAV is used to display and store the specific location of the damaged image.By optimizing the built-in parameters and improving the existing network structure,the model improves the classification accuracy and segmentation effect of pavement damage.Compared with the traditional single model,the integrated model proposed in this paper can not only provide the classification information of pavement damage,but also provide accurate location and geometric parameter information,which provides a new scheme for the automatic detection and maintenance of roads.At the same time,the complexity of UAV data acquisition also makes the detection model have a certain generalization ability,which is of reference value for other damage detection of road surface and bridge deck in the future.