Automatic threshold value image segmentation method based on entropy value and facing to transmission line part identification

Abstract

The invention discloses an automatic threshold value image segmentation method based on an entropy value and facing to transmission line part identification, comprising the following steps: converting an input transmission line colour image into a gray level image, and establishing a gray level histogram and an entropy value histogram aiming at the gray level image; determining a proper gray level stretching scheme according to the entropy value histogram, and stretching the gray level of the gray level image; repeating method for establishing the gray level histogram and the entropy value histogram in the last step, and reestablishing the entropy value histogram of the gray level image the gray level of which is stretched; finding an inflection point of entropy value saltation on an entropy value curve when the entropy value histogram appears to be a monotone increasing curve; evaluating the inflection point by a maximum distance method, wherein a gray level value corresponding to the inflection point is the optimal threshold value of the image threshold value segmentation; and changing the threshold value of the gray level image which is stretched by the optimal threshold value so as to complete the image segmentation. The invention has easy algorithm realization, low operation cost and high operation speed and can meet the need of the real-time preprocessing of a high-resolution image of the automatic line walking of transmission line taking the sky as a main background.

Description

technical field [0001] The invention relates to an automatic threshold value image segmentation method for real-time recognition of power transmission line components based on entropy value. The invention belongs to the high-resolution image preprocessing applied to the real-time identification of power transmission line components through machine vision, and in particular relates to using the information entropy value of the image to automatically select the image segmentation threshold to segment the foreground image composed of the main components of the power transmission line. , for automatic real-time identification of transmission line components. Background technique [0002] Transmission lines are the lifeblood of the national economy, and their safe and stable operation is crucial. Line inspection, that is, line inspection, is to grasp the line operation status and changes in the surrounding environment through inspections, so as to eliminate hidden dangers in tim...

AI Technical Summary

Problems solved by technology

However, in the real-time application of transmission line component recognition, manually setting the threshold for each image or a certain type of image cannot meet the needs of real-time recognition, and automatic threshold selection must be adopted.

[0012] Although automatic threshold selection has been widely concerned by the academic community in the past few decades, many automatic threshold selection methods have been produced, among which the famous iterative threshold selection method, Otsu method, minimum error threshold selection method, and through Drawing on morphological theory, the pixel value is regarded as the terrain height, and the image is regarded as the watershed algorithm (Watershed) of the terrain height map, but in practical applications, there is no method that can obtain impressive results for various images. Satisfactory results, and there is no theory to guide us how to automatically select the most "appropriate" threshold for different images to segment the image

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