Road disease picture enhancement method coupling traditional method and deep convolutional generative adversarial network

Abstract

The invention discloses a road disease picture enhancement method coupling a traditional method and a deep convolutional generative adversarial network. The traditional method is combined with the deep convolutional generative adversarial network to amplify data so as to achieve the effect of data enhancement. A manual method is used for carrying out collection, batch cutting and data set making on an original damaged pavement. According to the disclosed method, based on the generative adversarial network model, the convolutional neural network is used for replacing a multi-layer perceptron in the generative adversarial network, and through mutual game of the generator and a discriminator and continuous iteration updating, the generator has good pavement disease picture generation capability, and the discriminator has good picture authenticity identification capability. According to the road disease picture enhancement method, a new high-quality picture can be generated according to a real pavement disease picture, the purpose of expanding a data set is achieved, good conditions are provided for pavement disease recognition of deep learning, the effect of the trained model is better, and the generalization ability of the model is higher.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a road disease picture enhancement method coupled with a traditional method and a deep convolutional confrontation generation network. The invention is applied to data enhancement of road disease pictures. Background technique [0002] Cracks, potholes, ruts and other diseases caused by factors such as vehicle load and environment reduce the safety and comfort of driving, and it is necessary to maintain and repair the road surface in time. Due to the lack of sufficient basic theory and technical support, most pavement diseases are currently inspected through on-site inspections. This method requires a lot of time and resources, is inefficient, and cannot meet the needs of modern intelligent pavement inspections. With the development of artificial intelligence technology, machine learning methods are gradually applied to the field of road surface disease identification, which can re...

AI Technical Summary

Problems solved by technology

[0004] Due to the unconstrained and uncontrollable problems of the original adversarial generation network model, this invention proposes a road disease image enhancement method that couples the traditional method and the deep convolutional adversarial generation network, and uses a convolutional neural network to replace the adversarial generation Multilayer Perceptron in Networks

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