Unmanned aerial vehicle image ground fracture identification and extraction method based on machine learning

Abstract

The invention discloses an unmanned aerial vehicle image ground fracture identification and extraction method based on machine learning. The method comprises the steps of obtaining mining area land crack image data through unmanned aerial vehicle photogrammetry, each image data is cut into small image data with equal pixels, constructing an image data set with different background information according to clustering analysis, so that a learning sample of a land crack recognition model based on machine learning is established, and verifying the classification accuracy of the model in a cross manner by adopting a machine learning algorithm of a support vector machine and a one-leaving method. When crack information is extracted, identified crack-free images are changed into image data with afull-white background, crack extraction is carried out on identified crack-containing images to obtain gray scale image data with a white background and black crack information, and the two types ofimages are spliced according to a cutting sequence. According to the method, the problem that the application of a machine learning algorithm is limited due to the fact that mining area land surface information is too complex in mining area ground fracture recognition application is solved, and the real-time performance, precision and efficiency are high.

Description

technical field [0001] The invention relates to the field of engineering survey, in particular to a method for identifying and extracting ground fissures from unmanned aerial vehicle images based on machine learning. Background technique [0002] In the western region of my country, especially in the wind-blown sandy area in the west, ground fissures are one of the geological environmental problems caused by coal mining, causing deformation of buildings, damage to underground pipelines, damage to cultivated land, accelerated evaporation of soil moisture, destruction of vegetation, and soil erosion, etc. The problem has brought great difficulties to the mining area management workers, and it is also an important link in the land reclamation of the mining area. Therefore, it is necessary to obtain real-time, objective and high-precision distribution information of ground fissures in the mining area first, so as to assess the risk and study the development law of ground fissures...

AI Technical Summary

Problems solved by technology

The object-oriented method has achieved good results in the extraction of ground fissures, but its class and inheritance characteristics make it add a lot of extra work such as pointer operations to locate the function entry and maintain the virtual method table when using this method. This will make the processing efficiency of the program relatively inefficient, where the class is an abstraction of objects with the same characteristics (data elements) and behavior (function), inheritance is simply a hierarchical model

Methods such as edge detection and threshold segmentation will lead to a large number of noise points, and the accuracy of fracture extraction is poor, which affects the extraction effect of ground fissures

The method of manual visual interpretation is too complicated, and the efficiency is low, the timeliness is poor, and it is not popular and practical

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