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

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.