@inproceedings{da0f898ac4ca4bcf95e36d3170935d8d,
title = "Infrared image temperature measurement based on FCN and residual network",
abstract = "The key to electrolytic aluminum infrared temperature measurement system is to establish a certain mapping relationship between the gray image of the electrolyte and the temperature on the basis of the infrared thermal imaging system. Image segmentation techniques is use to get the extraction of the electrolyte region. Based on this, this paper combined the FCN network architecture, improved the VGG19 network with relatively good performance at present. In view of the network depth deepening, this paper also incorporates the residual thought, which solves the problem of network degradation caused by the deepening of the network. The new framework model proposed in this paper can meet the requirements of industrial temperature measurement.",
keywords = "FCN, Infrared physics, ResNet, Temperature measurement",
author = "Zhengguang Xu and Jinjun Wang and Pengfei Xu and Tao Liu",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2017 ; Conference date: 14-10-2017 Through 15-10-2017",
year = "2018",
doi = "10.1007/978-981-10-6499-9\_73",
language = "英语",
isbn = "9789811064982",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "769--775",
editor = "Weicun Zhang and Junping Du and Yingmin Jia",
booktitle = "Proceedings of 2017 Chinese Intelligent Systems Conference",
}