基于融合重构的电气设备红外图像EnFCM聚类分割方法  

EnFCM Clustering Segmentation Method for Infrared Image of Electrical Equipments Based on Fusion Reconstruction

作　　者：刘沛津 张香瑞 魏平 LIU Peijin;ZHANG Xiangrui;WEI Ping(Mechanical and Electrical Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China;Northwest Branch of State Grid Corporation of China,Xi'an 710048,China)

机构地区：[1]西安建筑科技大学机电工程学院,陕西西安701055 [2]国家电网有限公司西北分部,陕西西安710048

出　　处：《红外技术》2024年第3期295-304,共10页Infrared Technology

基　　金：国家自然科学基金(61903291);陕西省重点研发计划(2022GY-134)。

摘　　要：红外图像分割是电气设备红外故障诊断的关键环节,而电气设备的不均匀散热、较低的对比度与多源噪声的干扰,会导致目标区域过分割,严重影响分割精度。对此本文提出一种基于融合重构的EnFCM(Enhanced Fuzzy C-Means)聚类电气设备红外图像分割方法。首先对梯度图像进行自适应形态学重建操作,保证算法对噪声图像的分割能力;其次对图像进行显著性检测,将显著图与梯度图融合得到重构后的图像,凸显故障部位的特征,避免过分割;然后对重构后的图像进行分水岭分割获取超像素图像,最后对超像素图像直方图聚类得到分割结果。对电气设备红外图像的实验结果表明:本文算法在电气设备红外图像上能准确分割出故障区域,获取其位置与轮廓,有效改善了过分割现象,在选取的交并比与DICE系数指标对比中,本文方法对比选取的FRFCM、FCM、SFFCM、FCM_SICM、RSSFCA、AFCF平均提升了81%与79%;同时对噪声有较强的鲁棒性,在选取的分割准确率指标对比中,本文方法对比选取的FRFCM、FCM、SFFCM、FCM_SICM、RSSFCA、AFCF平均提升了73%,取得了较优的分割效果。Infrared image segmentation plays a pivotal role in diagnosing faults in electrical equipment using infrared imagery.However,uneven heat dissipation,lower contrast,and interference from multiple sources of noise in electrical equipment can lead to over-segmentation of the target region,seriously affecting segmentation accuracy.In this study,we propose an Enhanced Fuzzy C-Means(EnFCM)clustering method based on fusion reconstruction for infrared image segmentation of electrical equipment.First,the gradient image was subjected to an adaptive morphological reconstruction operation to ensure the segmentation ability of the algorithm on noisy images;second,the image was tested for saliency,and the reconstructed image was obtained by fusing the saliency map with the gradient map to highlight the features of the fault site and avoid over-segmentation;then,watershed segmentation was performed on the reconstructed image to obtain the super-pixel image;finally,the histogram clustering of the super-pixel image was obtained by segmentation.The experimental results on the infrared image of electrical equipment show that the algorithm in this paper can accurately segment the fault area on it,obtain its location and contour,and effectively improve the phenomenon of over-segmentation and in the comparison of the selected intersection and concatenation ratio and DICE coefficient indexes,this paper's method improves 81%and 79%on average compared to selected FRFCM,FCM,SFFCM,FCM_SICM,RSSFCA,and AFCF;meanwhile,it is extremely robust to noise,and in the comparison of selected segmentation accuracy indexes,this paper's method achieves segmentation results that are on average 73%superior compared to selected FRFCM,FCM,SFFCM,FCM_SICM,RSSFCA,and AFCF,thus,superior segmentation results were achieved.

关 键 词：红外图像 图像分割 自适应形态学重建 显著性检测 EnFCM 

分 类 号：TM507[电气工程—电器] TP391.4[自动化与计算机技术—计算机应用技术]