Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

Abstract

In order to solve the problem that the slope surface diseases cannot be accurately identified, which cannot be repaired in time and cause serious slope disasters, a slope intelligent recognition technology based on deep neural network is proposed. Based on convolutional neural network (CNN) theory, the technology adopts the transfer learning method to solve the overfitting problem of slope surface samples, which is difficult to obtain a large number of marked samples, and verifies the proposed model by experiment. The results are as follows: the recognition results of various slope surface diseases by ResNet-18 network are higher than AlexNet and VGG-16, with an average accuracy of 84.1%, and the recognition effect of cracks is the best. Under the same migration strategy, the detection accuracy of ResNet-18 is 96.3%, which is much higher than the other two, and the detection time is reduced by 15% on average. It is proved that the ResNet-18 model proposed can identify slope changes very effectively, so that workers can be timely dispatched for maintenance, reducing the possibility of disaster, which has great significance.