A Method for Segmentation of Transformer Oil Level Region Based on Infrared Image. Issue 1 (October 2021)

• Record Type: Journal Article
• Authors: Chen, Ying; Fan, Songhai; Liu, Yicen; Ma, Xiaomin; Shi, Zhu
• Is Part Of: IOP conference series. Volume 898:Issue 1(2021)
• Publication Date: 2021-10
• Subjects: Infrared image -- Maximum entropy threshold segmentation algorithm -- SLIC super pixel segmentation algorithm; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/898/1/012027
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.