Recognition of highway subgrade collapse disease based on shallow feature fusion of ground penetrating radar images

In order to prolong the service life of highway, a recognition method of subgrade collapse disease based on shallow feature fusion of ground penetrating radar images was proposed. Based on electromagnetic wave theory and Maxwell equation, GPR images and forward simulation images of highway subgrade were obtained. After acquiring the images, the spatial weighted color histogram was used to obtain the shallow features of the two images, and the features were input into the convolutional neural network, in which the features were fused and the subgrade disease recognition of the highway was completed. Experiments showed that the convolutional neural network used in this method had a stronger ability to identify roadbed collapse disease, and the disease recognition effect was better when ground penetrating radar images and forward simulation images were combined.