文献类型：期刊文献

英文题名：Fire detection in video surveillance using superpixel-based region proposal and ESE-ShuffleNet

作者：Wang, Pengyu[1];Zhang, Jianmei[1];Zhu, Hongqing[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：82

期号：9

起止页码：13045

外文期刊名：MULTIMEDIA TOOLS AND APPLICATIONS

收录：;EI(收录号：20213710876589);WOS:【SCI-EXPANDED(收录号:WOS:000695537100004)】；

基金：This work was supported by the National Nature Science Foundation of China under Grant 61872143.

语种：英文

外文关键词：Forest fire detection; Cauchy mixture model; Superpixel; Light-weight network; ShuffleNet

摘要：This paper proposes a forest fire detection framework using superpixel-based suspicious fire region proposal and light-weight convolutional neural network. The proposed methodology contains two main steps. In suspicious fire region proposal, we introduce a novel superpixel algorithm (SCMM) driven by Cauchy mixture model. Then, the negative Under-segmentation Error (UE) of each superpixel is applied to inter-frame comparison for predicting varying superpixels. After that, by computing the features of motion superpixels using Local Difference Binary (LDB) descriptor for two adjacent frames, the suspicious fire regions are localized. In following fire identification, to improve network performance while reducing computational complexity, this study presents a light-weight network architecture, called Expanded Squeeze-and-Excitation ShuffleNet (ESE-ShuffleNet). All suspicious fire regions are sent into this network to identify as either fire or non-fire included. Experiments show that our framework performs well on fire detection tasks. Code is available at http://www.imagetech-polynomials.com/ESE-Shuff.leNet.html.