Research Of Infrared Image Segmentation For Power Equipment In Substation

Infrared imaging technology is a proven means of on-line monitoring for electrical equipment, while the construction of intelligent on-line monitoring system which is based on infrared imaging technology is the direction of developing electrical equipment monitoring system. Infrared image segmentation is an important component of software modules in system and key step to achieve the function of intelligent monitoring. The characteristics of electrical equipment extracted through segmentation can be used as a basis for the system to make intelligent judgments and decisions in the later stage.This paper is based on substations'infrared monitoring system, aims at enhancing the automation and intelligence level of this system, and therefore pays its main attention to the substations'infrared image segmentation technology. Firstly, the current image segmentation algorithms of? visible image have been classified and summarized in the paper, and the characteristics and the weak point of the algorithms are discussed through experiment comparison. This paper also attaches importance to new image segmentation algorithms. Finally, Watershed segmentation algorithm and fuzzy clustering segmentation algorithm have been selected for segmenting infrared image in this article based on above research work. To be effective and accurate detection of oil level, this paper processed infrared image of the Oil Conservator with the improved watershed segmentation algorithm which has taken the multi-scale morphological gradient image as inputting image of watershed transform. On the premise of focusing on regional connectivity, multi-scale morphological gradient image has been binarization by the method of Otsu to obtain preliminary seed map. After the removal of false minimum marks, we make the exact minimum marks of the final seed map as the starting point of Watershed transform to accomplish image segmentation. Experimental results show that proposed method in this article can separate the oil level of Oil Conservator effectively and accurately. therefore, it can be used as an one of the important component of substation's on-line monitoring system. Taking into account the internal information complexity and relevance of Infrared image, this paper has conducted a study of Kernel-based fuzzy C-means segmentation algorithm yet. Improved Robust Kernelized Fuzzy C-means(IRKFCM) can enhanced the rationality of segmentation by adding spatial information. And this paper also proposed the clustering center initialization method which can enable the clustering iteration parameters quickly converge to reasonable division parameter values. Experimental results show that IRKFCM algorithm can separate malfunction location of Radiator and calculate the size of malfunction region effectively and accurately compared to other fuzzy clustering method.