基于改进YOLOv5s的火焰烟雾检测方法  

Flame smoke detection method based on the improved YOLOv5s

作　　者：孙剑 张数数 SUN Jian;ZHANG Shushu(College of Computer and Information Technology,Xinyang Normal University,Xinyang 464000,China)

机构地区：[1]信阳师范大学计算机与信息技术学院,河南信阳464000

出　　处：《信阳师范大学学报(自然科学版)》2025年第2期152-158,共7页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学青年基金项目(62403405);河南省科技攻关项目(222102210300)。

摘　　要：针对传统火焰烟雾检测不及时、小目标烟火检测困难和检测精度低等问题,提出了一种改进YOLOv5s的火焰烟雾检测方法。首先,在YOLOv5s模型的Backbone层中引入了SE注意力机制,自适应地调整每个通道的特征权重,增强有用特征并抑制无用特征,从而提升网络对火焰烟雾特征的提取能力。其次,在YOLOv5s模型的Neck层中引入了BiFPN模块作为特征融合模块,通过BiFPN模块引入双向连接,结合自底向上和自顶向下的特征融合方式,能够充分利用不同层级的特征信息,提高特征的丰富性。最后,将改进YOLOv5s模型应用于实际火焰烟雾数据集上。实验结果表明:改进YOLOv5s模型的准确率、召回率和mAP值分别提升了1.8%、2.6%和1.5%,能够满足火焰烟雾检测的精度要求。To solve the problems of untimely traditional flame smoke detection,difficult pyrotechnic detection and low detection accuracy of small targets,an improved flame smoke detection method of YOLOv5s was proposed.Firstly,the SE attention mechanism was introduced into the backbone layer of the YOLOv5s model,which can adaptively adjust the feature weight of each channel,enhance the useful features and suppresses the useless features,improve the network’s ability to extract the features of flame and smoke.Secondly,BiFPN module was introduced as a feature fusion module in the Neck layer of the YOLOv5s model,and the bidirectional connection was introduced through BiFPN module,which can make full use of the feature information of different levels and improve the richness of features by combining the bottom‑up and top‑down feature fusion methods.Finally,the improved YOLOv5s model was applied to the actual flame smoke dataset,and the experimental results showed that the accuracy,recall rate and mAP value of the improved YOLOv5s model were increased by 1.8%,2.6%and 1.5%,respectively,which can meet the accuracy requirements of flame smoke detection.

关 键 词：火焰烟雾检测 YOLOv5s模型 SE注意力机制 BiFPN模块 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]