Research on intelligent detection of pavement damage based on CNN

Abstract

In order to timely detect road damage under complex constraints, the road damage mechanism was used to reclassify the types of road damage, the VGG-19 model was applied to identify and detect less road damage images intelligently through convolutional neural network and transfer learning and at the same time, the road damage situation was detected using the Softmax classifier and the feasibility and accuracy of the method were verified on the basis of the detection set. The results show that the detection and recognition accuracy of the model proposed in this paper reaches 86.2 %, an increase of 6.2-49.8 percentage points than the detection results of other convolutional neural network models. Therefore, it can be concluded that the transfer learning-and convolutional neural network-based road damage intelligent detection methods proposed in this paper are feasible, and this research is helpful to realize high-precision real-time intelligent detection of road damage.