Real-time visual detection and identification method for high speed rail surface defect

Abstract

A real-time visual detection and identification method for high speed rail surface defects comprises the following steps of: (1) image acquisition; (2) image preprocessing; (3) defect preliminary detection, that is, performing logic or operation combination of detection results based on gray scale compensation with detection results based on top-hat operation, and detecting whether an abnormal area exists in an image, if not, finishing the detection, or else continuing the processing; (4) defect accurate positioning, that is, accurately positioning the defect by an algorithm of bonding single defect, an algorithm of filling holes in the defect area, and an algorithm of selecting the main defect, and extracting the defect area through marks; (5) defect classification, that is, extracting and selecting characteristics of the defect area, designing and training a BP neural network, and classifying the defects by the BP neural network. The invention has the advantages of simple principle, high automation degree, high detection speed, and high detection precision.

Description

technical field [0001] The invention mainly relates to the field of online visual detection, in particular to a real-time visual detection and identification method for surface defects of high-speed rails. Background technique [0002] In order to adapt to the rapid development of my country's modern railways and ensure the safety of railway operations, the railway department has put forward strict requirements on the track quality of the railway site. At present, for the detection of rail defects, there are two methods: the electronic detection method based on light, electricity and magnetic signals, and the visual detection method based on the principle of machine vision. [0003] The defect detection of domestic on-site railway tracks basically adopts electronic detection method, and almost does not use visual detection method. The electronic detection method is generally used to detect the internal defects of the rail, and its detection accuracy is low, so it is only su...

AI Technical Summary

Problems solved by technology

In the high-speed state, the camera may move or rotate at a small angle with the vibration of the train, and factors such as natural light, site environment, and weather may introduce noise, shadow bands, and "flare" areas to the railroad track image, making general detection methods easy Affected by disturbance factors

[0009] (2) The shape

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