关键词: 绝缘子, 红外热像, 多元分类, Softmax回归, 概率阈值

Abstract: This paper presents a characteristic infrared thermal imaging combined with Softmax regression methods to identify insulator contamination levels. Through the infrared images of gray, image filtering, binarization, disk partition, semi-disk extraction pretreatment process, a single semi-insulator disk area is obtained. It designs with 6 characteristics of environment temperature, the average gray background image insulator, insulator disk region average gray, gray distribution of insulator disk, gray entropy variance and environment humidity that reflects contamination level based Softmax regression multiple classification model to identify insulator contamination levels. Probability threshold is introduced starting from the source of the problem to solve the resulting improper shooting invalid insulator infrared image on the contamination level of classification. Experimental results show that the selected feature sets and insulator contamination recognition model are efficient and feasible.

Key words: insulator, infrared thermal image, multi-class classification, Softmax regression, probability threshold