A method for identifying lesion types of gpr images

Abstract

The invention discloses a method for identifying the disease type of a GPR image, which comprises the following steps: Step 1: Aiming at the GPR image with disease reflection waves, normalizing the GPR image pixels to obtain a matrix NI; Step 2: performing matrix NI Histogram equalization processing to obtain the matrix NI_H; Step 3: Perform ternarization on the matrix NI_H, and the ternary threshold is set according to the maximum inter-class variance double threshold method to obtain the matrix NI_HB; Step 4: Calculate each column of the matrix NI_HB Then make the phase adjacent elements in each column have different signs, and finally get two phase types; Step 5: Select the phase type with a large number as the GPR image disease type. The beneficial effects of the present invention are: realizing the highlighting of disease features, facilitating the extraction of disease features, realizing the highlighting of common underground diseases of expressways, facilitating the process of disease interpretation, saving costs, and meeting the pursuit goal of automatic disease identification in the ground penetrating radar industry, has great practical significance.

Description

technical field [0001] The invention relates to a method for identifying disease types of GPR (Ground-penetrating radar, ground-penetrating radar) images, and belongs to the technical field of disease identification. Background technique [0002] In the interpretation of roadbed diseases of GPR signal expressway, the most widely used method at home and abroad is manual image interpretation. However, manual interpretation of images relies heavily on the experience of the interpreter and is highly subjective. When the amount of data is large, the manual interpretation cycle is very long and has a certain lag. How to automatically identify the type of disease and give feedback to save interpretation time has become one of the urgent needs of GPR signal road subgrade image interpretation. The present invention provides a new solution based on the automation of GPR data disease detection and classification. Other existing patents such as Patent No. CN104698503A use offset corre...

AI Technical Summary

Problems solved by technology

This method is suitable for the search of tubular targets, and the use of manual methods to find the target body is time-consuming and labor-intensive

Another example is the patent number CN1595195A, which uses RBF neural network to automatically identify the target category of radar data. The premise is to analyze and extract the characteristics of the target, and a large amount of sample data is required. However, in the actual implementation process, a large amount of sample data is usually difficult to obtain.

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