Open AccessResearch on Recognition and Location Method of Insulator in Infrared Image Based on Deep Learning
Author(s) -
Hua Huang,
Yongxi Huang,
Xiaojing Mu,
Xiaozhou Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2087/1/012090
Subject(s) - thermography , infrared , insulator (electricity) , artificial intelligence , computer science , pattern recognition (psychology) , computer vision , materials science , optoelectronics , optics , physics
Infrared thermography technology is widely used in the thermal condition detection of insulators due to its advantages of non-contact, sensitive, online detection. To realize the automatic detection of the operating condition of insulators in complex environments, this paper proposes a method for the recognition and location of the insulator based on Region-based Fully Convolutional Networks (R-FCN). The model was trained and tested on the constructed insulator infrared data set, compared with the SSD model. The results showed that the R-FCN detecting insulators can not only accurately locate insulators, but have an AP (average precision) value as high as 89.2%. Therefore, the findings in this paper have verified that R-FCN has great advantages in the recognition and location of infrared images of insulators and has practical application value.