文献类型：期刊文献

英文题名：ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition

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

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

年份：2021

卷号：9

起止页码：10858

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20210409805500);WOS:【SCI-EXPANDED(收录号:WOS:000609799300001)】；

基金：This work was supported in part by the National Nature Science Foundation of China under Grant 61872143, and in part by the Natural Science Foundation of Shanghai under Grant 19ZR1413400.

语种：英文

外文关键词：Feature extraction; Forestry; Shape; Convolution; Task analysis; Image segmentation; Image color analysis; Forest fire detection and recognition; attention U-Net; SqueezeNet; fire module; light-weight network

摘要：Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.