改进GSO与二维OTSU融合的红外图像多阈值分割方法

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

针对二维最大累间方差(Nobuyuki OTSU,OTSU)图像分割算法在电气设备故障诊断与定位中,其红外图像的多阈值分割中存在的耗时多、分割精度低、误分割等不足,造成故障区域欠分割或者过分割的问题,提出一种改进的萤火虫算法(glowworm swarm optimization,GSO)与二维OTSU的融合算法来提高电气设备红外图像多阈值分割的实时性与准确度。寻优过程中,将局部寻优扩展到全局寻优,并引入非线性递减步长及新的移动策略对GSO进行优化改进。实验结果表明:该融合算法在分割结果上较二维OTSU及未改进GSO与二维OTSU融合算法更能准确分割运行电气设备图像异常区域,分割速度分别提高19倍、1.28倍,为红外图像早期故障的有效识别与定位奠定基础。

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 multithreshold 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 multithreshold 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.