A hybrid attention model based on first-order statistical features for smoke recognition  

作　　者：GUO Nan LIU JiaHui DI KeXin GU Ke QIAO JunFei 

机构地区：[1]Beijing Laboratory of Smart Environmental Protection,Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing Artificial Intelligence Institute,Beijing 100124,China [2]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China

出　　处：《Science China(Technological Sciences)》2024年第3期809-822,共14页中国科学（技术科学英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No. 2021ZD0112302);the National Natural Science Foundation of China(Grant Nos. 62076013, 62021003, 61890935);CAAI-Huawei MindSpore Open Fund(Grant No. CAAIXSJLJJ-2021-016A)。

摘　　要：Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to misjudgment.To tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the proposed method outperformed popular deep CNNs and exis

关 键 词：hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] X932[环境科学与工程—安全科学]