Open AccessInfrared image detection of insulators based on Centernet model
Author(s) -
Yong Li,
Yongheng Ku,
Yaohui Cui,
Zhansheng Tian,
Wencao Sun
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 - 10.1088/1742-6596/2031/1/012009
Subject(s) - insulator (electricity) , infrared , fault detection and isolation , computer science , set (abstract data type) , artificial intelligence , pattern recognition (psychology) , remote sensing , materials science , physics , optics , optoelectronics , geology , actuator , programming language
The infrared image can reflect the temperature information inside the insulator, which is helpful to realize the follow-up fault diagnosis. However, insulators are usually located in remote areas, and the complex background will affect the accuracy of the algorithm model. Therefore, we established an infrared dataset of insulators. We introduced the Centernet network to infrared detection of insulators, and conducted training and testing on the data set. The results show that our proposed model has a significant improvement of 4% to 5% compared with several mainstream detection models. This can meet the needs of insulator fault detection.