基于多尺度显著性的GIS腔体内部异物智能识别

Intelligent Recognition of Foreign Matter in GIS Compartment Based on Multi-scale Significance

针对GIS腔体人工直接检修困难以及内部环境复杂,文中提出了一种适用于GIS腔体内部特殊环境的机器视觉异物识别算法。由于腔体内存在异物大小不一,文中采用改进的多尺度的F T算法,并且使用Gini指数选取最优的显著图,采用了自适应阈值分割的办法,分割出异物,以图像的开运算剔除细小的噪声;最后,使用改进的分类网络,对异物进行识别。相对于人工检测,此种算法在细小异物的识别方面具有更高的准确率,且在检测速度上更加有优势,综合检测正确率不低于94%,速度不低于50 fps。

In view of the difficulty of direct manual maintenance and the complexity of internal environment of GIS compartment,a kind of a machine vision foreign matter recognition algorithm,which is suitable for the special envi-ronment of GIS compartment,is proposed in this paper.Since the size of the foreign matter in the compartment is dif-ferent,the improved multi-scale FT algorithm is adopted in this paper.The Gini index is used to select the optimal significant graph.The way of adaptive threshold segmentation is used to segment foreign matters and the small noises is eliminated by image opening operation.Finally,the improved classification network is used to identify foreign mat-ter.This algorithm,compared with manual detection,has higher accuracy in the field of recognition of small foreign matters and more advantages in detection speed.The comprehensive detection accuracy is not less than 94%and the speed is not less than 50 fps.