现代电子技术2025，Vol.48Issue(5)：53-58,6.DOI:10.16652/j.issn.1004-373x.2025.05.009
基于双重注意力机制优化的C3D视频火灾烟雾分类方法
Video fire smoke classification method based on optimized C3D by dual attention mechanism
摘要
Abstract
The existing studies generally focus on specific categories of fire smoke and develop model algorithms to improve the accuracy of fire identification,but do not classify fires accurately.In the event of a fire,the clear fire category has a guiding effect on the subsequent fire suppression and rescue activities.In this paper,four standard fire experiments are carried out to establish video image data sets of four basic types of fires(wood pyrolysis smold fire,cotton rope smold fire,polyurethane foam fire,and n-heptane oil fire),and a video fire smoke classification model based on optimized C3D(Convolutional 3D)convolutional network is proposed.A dual SE(squeeze and excitation)attention module is introduced to improve the feature extraction capability of the model.A global average pooling(GAP)layer is adopted to replace the traditional full connection layer,which reduces model parameters,prevents overfitting,and improves the robustness of the model.The experimental results show that the accuracy rate of the optimized C3D model in identifying the types of fire smoke is 98.9%,which is 9.28% higher than that of the original model.In addition,the number of the model parameters is reduced by 64.39% .To sum up,the research can provide important application value for fire smoke monitoring and early warning.关键词
深度学习/烟雾分类/C3D/注意力机制/火灾识别/准确度Key words
deep learning/smoke classification/C3D/attention mechanism/fire identification/accuracy rate分类
电子信息工程引用本文复制引用
朱家哲,何豪,阳书林,杨智,黄冬梅..基于双重注意力机制优化的C3D视频火灾烟雾分类方法[J].现代电子技术,2025,48(5):53-58,6.基金项目
浙江省"尖兵""领雁"研发攻关计划项目:工业企业安全生产智能防控关键技术-工业企业火灾灾变机理与感知预警处置一体化技术研究及应用(2024C03252) （2024C03252）
2024年度应急管理研发攻关科技项目:基于时空AI的火灾图像特征挖掘机类型辨识方法研究(2024YJ007) （2024YJ007）
