Steel rail scale damage detection method based on deep learning

Abstract

The invention discloses a steel rail scale damage detection method based on deep learning. The method comprises the following steps: firstly, dividing a data set formed by all images into a training set, a test set and a verification set according to a set proportion; setting a network structure and performing forward propagation, and performing deep learning training by using a residual neural network, the residual neural network comprising a convolutional layer, a pooling layer and a full connection layer; after a calculation result of forward propagation is output, calling a reverse propagation algorithm; and finally, reserving a model trained by the final residual neural network, and drawing a change curve of each parameter in the whole training process for reference. According to themethod, a convolutional neural network technology in machine vision and deep learning is combined, features of a steel rail scale damage sample are extracted and learned and classified, and a model output by the neural network is used for judgment in the actual industry; compared with a method for judging the fish scale damage on the surface of the steel rail by using a manual method in the industrial field, the method has very high detection efficiency and accuracy.

Description

technical field [0001] The invention relates to machine vision technology, deep learning convolutional neural network technology and image classification technology, and in particular to a method for detecting fish scale damage on rails based on deep learning. Background technique [0002] Image classification is to give an input image and use a classification algorithm to determine the category of the image. It is at the heart of computer vision and has a wide range of practical applications. Depending on the basis of division, the result of image classification will be different. Its main process is to judge the category of an input image through preprocessing, feature extraction, and classifier training. Among them, the image preprocessing operation is to facilitate subsequent operations such as feature extraction and other methods such as filtering and scale normalization. Feature extraction is to extract corresponding features according to a certain established image...

AI Technical Summary

Problems solved by technology

Because of its inhomogeneity and asymmetry, its detection has long consumed a lot of manpower and time

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