@inproceedings{d49b5a62daf444788311990fdff1da12,
title = "Detection method of small foreign matter in transformer based on small target enhancement and contrast learning",
abstract = "At present, the use of robots to carry out transformer internal inspection work has been related research, if the intelligent detection of small targets such as foreign bodies and small discharge traces inside the transformer can be realized, the efficiency of robot internal inspection will be greatly improved. For small target detection, the current popular method in the industry is to improve the detection accuracy by optimizing the structure of the network model, but the disadvantage is that it increases the difficulty of the algorithm design and the computational complexity. In this paper, based on the Faster-RCNN model, small target enhancement and contrast learning methods are proposed for small target detection in the industrial field under the premise of ensuring the detection accuracy of large-scale targets. The experimental results on the transformer internal inspection data set show that our proposed method is superior to the existing methods. It provides a new solution to the problem of improving the recognition effect of small targets.",
keywords = "Target Enhancement Contrastive learning Small foreign matter inside the transformer, deep learning, small target detection",
author = "Su Lei and Zhou Haoyi and Huang Hua and Zhang Wancai and Cao Boyuan",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE. All rights reserved.; 2022 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2022 ; Conference date: 19-08-2022 Through 20-08-2022",
year = "2022",
doi = "10.1117/12.2662565",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Shi-Jinn Horng",
booktitle = "International Workshop on Automation, Control, and Communication Engineering, IWACCE 2022",
address = "美国",
}