학술논문
基于级联稀疏查询机制的轻量化火灾检测算法 / An improved lightweight fire detection algorithm based on cascade sparse query
Document Type
Academic Journal
Author
Source
光电工程 / Opto-Electronic Engineering. 50(10):104-120
Subject
Language
Chinese
ISSN
1003-501X
Abstract
针对现有火灾检测算法仍存在的模型复杂、检测速度慢、误检率高等问题,提出一种基于级联稀疏查询机制的轻量化火灾检测网络LFNet.首先,建立了轻量化的图像特征提取模块ECDNet,其通过在YOLOv5s主干网络中嵌入轻量化注意力模块ECA(efficient channel attention),用于解决火灾检测中火焰与烟雾的多尺度难点;其次,利用深层特征提取模块FPN+PAN,对不同层级的特征图进行深度处理和多尺度融合;最后,利用嵌入轻量化的级联稀疏查询模块CSQ(cascade sparse query)提升对早期火灾中的小火焰与薄烟雾的检测准确率.实验表明,本文方法在mAP和Precision等客观指标上的综合表现达到最优,同时在实现较高检测精度时的参数量也较低,能够满足实际场景的火灾检测要求.
To address the challenges of complex models,slow detection speed,and high false detection rate during fire detection,a lightweight fire detection algorithm is proposed based on cascading sparse query mechanism,called LFNet.In the study,firstly,a lightweight feature extraction module ECDNet is established to extract more fine-grained features in different levels of feature layers by embedding the lightweight attention module ECA(efficient channel attention)in YOLOv5s backbone network,which is used to solve the multi-scale of flame and smoke in fire detection.Secondly,deep feature extraction module FPN+PAN is adopted to improve multi-scale fusion of feature maps at different levels.Finally,the Cascade Sparse Query embedded lightweight cascade sparse query module is applied to improve the detection accuracy of small flames and thin smoke in early fires.Experimental results show that the comprehensive performance of the proposed method in objective indicators such as mAP and Precision is the best on SF-dataset,D-fire and FIRESENSE.Furthermore,the proposed model achieves lower parameters and higher detection accuracy,which can meet the fire detection requirements of challenge scenes.
To address the challenges of complex models,slow detection speed,and high false detection rate during fire detection,a lightweight fire detection algorithm is proposed based on cascading sparse query mechanism,called LFNet.In the study,firstly,a lightweight feature extraction module ECDNet is established to extract more fine-grained features in different levels of feature layers by embedding the lightweight attention module ECA(efficient channel attention)in YOLOv5s backbone network,which is used to solve the multi-scale of flame and smoke in fire detection.Secondly,deep feature extraction module FPN+PAN is adopted to improve multi-scale fusion of feature maps at different levels.Finally,the Cascade Sparse Query embedded lightweight cascade sparse query module is applied to improve the detection accuracy of small flames and thin smoke in early fires.Experimental results show that the comprehensive performance of the proposed method in objective indicators such as mAP and Precision is the best on SF-dataset,D-fire and FIRESENSE.Furthermore,the proposed model achieves lower parameters and higher detection accuracy,which can meet the fire detection requirements of challenge scenes.