Multi-threshold segmentation method of infrared images based on improved fusion of GSO and 2D OTSU

LIU Peijin; WANG Xi; HE Ning

doi:10.5768/JAO202142.0402006

Journal of Applied Optics > 2021 > 42(4): 671-677. > DOI: 10.5768/JAO202142.0402006

LIU Peijin, WANG Xi, HE Ning. Multi-threshold segmentation method of infrared images based on improved fusion of GSO and 2D OTSU[J]. Journal of Applied Optics, 2021, 42(4): 671-677. DOI: 10.5768/JAO202142.0402006

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Multi-threshold segmentation method of infrared images based on improved fusion of GSO and 2D OTSU

School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

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Received Date: February 06, 2021
Revised Date: March 21, 2021
Available Online: May 28, 2021

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Abstract

Abstract

Aiming at the problem of under-segmentation or over-segmentation in the fault area caused by the shortcomings of more time-consuming, low accuracy of segmentation and mis-segmentation in the multi-threshold segmentation of infrared images in electrical equipment fault diagnosis and location based on the two-dimensional (2D) OTSU image segmentation algorithm, an improved fusion algorithm of glowworm swarm optimization (GSO) and 2D OTSU was proposed to improve the real-time and accuracy of multi-threshold segmentation of infrared images for electrical equipment. In the optimization process, the local optimization was extended to the global optimization, and the nonlinear degressive step size and the new shifting strategy were introduced to optimize and improve the GSO. The experimental results show that the proposed fusion algorithm is more accurate than 2D OTSU and unimproved GSO with 2D OTSU fusion algorithm to segment the image abnormal area of operational electrical equipment in segmentation results, and the segmentation speed can be improved by 19 times and 1.28 times, which lays a foundation for the effective identification and location of early fault in infrared images.

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References

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Multi-threshold segmentation method of infrared images based on improved fusion of GSO and 2D OTSU