Convolutional neural network-based asphalt pavement crack classification and recognition method

Abstract

The present invention discloses a convolutional neural network-based asphalt pavement crack classification and recognition method. Road cracks are classified according to different repair strategies of cracks of different widths and shapes; sample pictures are marked correspondingly and preprocessed so as to train a constructed convolutional neural network; and the trained convolutional neural network is adopted to classify crack information in pictures, and the severity levels of cracks are divided according to the widths and shapes of the cracks. Since the crack information in the pictures is automatically classified according to a pre-classification mode, and the severity levels of the cracks are divided, and therefore, efficiency of crack identification can be improved, road maintenance and repair work can be greatly facilitated; the convolutional neural network algorithm is used as a classifier to classify the road cracks; the convolutional neural network is a layered neural network composed of convolutional layers and sampling layers which are alternately distributed; and the convolutional neural network can learn features from training data implicitly and has greater advantages in the classification of cracks with no regular and significant features.

Description

technical field [0001] The invention belongs to the technical field of classification and recognition of road cracks, and in particular relates to a method for classification and recognition of asphalt pavement cracks based on a convolutional neural network. Background technique [0002] In recent years, my country's road construction has developed on a large scale. At the same time, road maintenance has become an important work content, and the detection and classification of cracks account for a large part of maintenance. During the road use process, the service life of the road surface will gradually decrease with the influence of the vehicle load and the surrounding environment, resulting in greatly reduced road use efficiency and vehicle driving safety. There are many reasons for cracks in roads, and different reasons generate different shapes of cracks, and for cracks with different widths and shapes, the repair strategies are very different. Nowadays, the consumption...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a method for classification and identification of asphalt pavement cracks based on convolutional neural network, which solves the disadvantages of mainly relying on manpower to identify road cracks, and uses deep learning algorithm to train convolutional neural network to improve the accuracy and reliability of crack classification , and divide the severity level of cracks, which intuitively reflects the severity of cracks in the image, which provides great convenience for the research on the degree of road damage and the formulation of crack repair strategies

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