Bat algorithm and Otsu algorithm-based infrared image segmentation method and system

Abstract

The invention discloses a bat algorithm and Otsu algorithm-based infrared image segmentation method. The method comprises the steps of S10: obtaining a to-be-segmented infrared image; S20: taking an interclass variance function in an Otsu algorithm as a target function of a bat algorithm, and taking a segmentation threshold variable corresponding to the interclass variance function as a bat position variable in the bat algorithm; and S30: based on the bat algorithm, calculating a corresponding bat position when the target function is maximal, and segmenting the to-be-segmented infrared image into a background region and a target region by taking the bat position as an optimal segmentation threshold. In addition, the invention discloses a corresponding system. The to-be-segmented infrared image can be segmented quickly. Compared with an existing infrared image threshold segmentation method, the threshold selection speed is effectively increased and a foundation is laid for real-time extraction and analysis of temperature field characteristics of subsequent equipment.

Description

technical field [0001] The invention relates to infrared image segmentation technology, in particular to an infrared image segmentation method and system based on Otsu algorithm. Background technique [0002] Infrared images of power equipment are pseudo-color images, which have the characteristics of concentrated intensity and low contrast. Traditional image segmentation algorithms often cannot separate the target from the background well. If the region of interest is artificially delineated, the efficiency of the intelligent diagnosis system will obviously be greatly reduced. The research on image threshold automatic selection method has attracted many scholars for a long time, and it is the common goal of these researchers to find a simple, practical, self-adaptive threshold automatic selection method. The Otsu algorithm (also known as the maximum inter-class variance method or Otsu method) proposed by Otsu in 1979 has been considered as one of the optimal methods for au...

AI Technical Summary

Problems solved by technology

[0003] However, in actual images, although the Otsu algorithm has better segmentation performance, the amount of calculation is very huge

Although the Otsu algorithm effectively solves the problem of difficult threshold selection in the process of binarization and segmentation of infrared images, it often cannot meet the needs of real-time feature extraction and analysis of the infrared temperature field of subsequent equipment in terms of threshold segmentation speed

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