Open AccessA Method for Segmentation of Transformer Oil Level Region Based on Infrared Image
Author(s) -
Ying Chen,
Songhai Fan,
Yicen Liu,
Xiang Ma,
Shi-Liang Zhu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/898/1/012027
Subject(s) - artificial intelligence , segmentation , computer vision , image segmentation , computer science , pixel , entropy (arrow of time) , threshold limit value , infrared , transformer , pattern recognition (psychology) , channel (broadcasting) , engineering , telecommunications , voltage , electrical engineering , optics , medicine , physics , environmental health , quantum mechanics
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.