[1]张坚鑫,郭四稳,张国兰,等.基于多尺度特征融合的火灾检测模型[J].郑州大学学报(工学版),2021,42(05):13-18.[doi:10.13705/j.issn.1671-6833.2021.05.016]
　Zhang Jianxin,Guo Si Jing,Zhang Guolan,et al.Fire Detection Model Based on Multi-scale Feature Fusion[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):13-18.[doi:10.13705/j.issn.1671-6833.2021.05.016]
点击复制

基于多尺度特征融合的火灾检测模型()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年05期
页码:
13-18
栏目:
出版日期:
2021-09-10

文章信息/Info

Title:
Fire Detection Model Based on Multi-scale Feature Fusion
作者:
张坚鑫; 郭四稳; 张国兰; 谭琳
广州大学计算机科学与网络工程学院;
Author(s):
Zhang Jianxin; Guo Si Jing; Zhang Guolan; Tan Lin;
School of Computer Science and Network Engineering, Guangzhou University;
关键词:
Keywords:
deep learning;  fire detection;  convolutional neural network;  multi-scale features;  feature pyramid network
DOI:
10.13705/j.issn.1671-6833.2021.05.016
文献标志码:
A
摘要:
火灾的发生对于人民的生命及财产安全都会造成较大的危害，随着科技的进步，火灾检测设备与方法层出不穷。为了提高火灾检测的准确率，降低火灾的漏报率，使用图像型火灾检测方法逐渐成为主流的检测方法，深度学习的发展更是为火灾检测提供了新的思路与方法。改进的两场景检测模型 Faster R-CNN能够在火灾检测中发挥巨大的作用。首先使用 Resnet101 作为特征提取网络，接着使用特征金字塔结构 FPN 提取了 Resnet101 的浅层特征和高层特征，将 Resnet101 的浅层特征图输入 Inception Module 结构提取多种尺寸的卷积特征，最终使用像素注意力机制和信道注意力机制对目标位置进行强化并弱化其余部分，使得检测目标更加精确。该网络避免了主干网络特征提取不充分的问题，融合了多种尺度的特征来区分火灾区域和非火灾区域，有效提高了火灾图像数据集的检测准确率。该模型的平均检测准确率 MAP 相对于Resnet101 特征提取网络有了9个百分点的提升，相对于 Resnet101 加入特征金字塔结构网络 FPN 提升了5个百分点。该模型的检测效率与 Resnet101 特征提取网络、Resnet101 加入特征金字塔结构网络 FPN 都接近，检测一张火灾图像所需时间为0.86秒。该模型能够检测出室内和室外火灾的目标区域，精度相对原始模型得到较大的提升，效率与原始模型接近。
Abstract:
This paper aims to modify the two-scene detection model Faster R-CNN. Specifically, this model uses Resnet101 to extract features which are processed by pyramid structure FPN to extract the shallow and high-level features of Resnet101. The shallow feature map of Resnet101 is input into Inception Module structure to obtain the convolutional features of multiple sizes, and finally the proposed model uses the pixel attention mechanism and channel attention mechanism to emphasize the target position and weaken the rest, which makes the detection target more accurate. This network avoids the problem of insufficient feature extraction of trunk network, and integrates features of various scales to distinguish fire area and non-fire area, thus effectively improves the detection accuracy of fire image data sets, and mean average precision MAP is 0.851.

参考文献/References:

[1] 杨文柱,刘晴,王思乐,等.基于深度卷积神经网络的羽绒图像识别[J].郑州大学学报(工学版),2018,39(2):11-17.

[2] 黄文锋,徐珊珊,孙燚,等.基于多分辨率卷积神经网络的火焰检测[J].郑州大学学报(工学版),2019,40(5):80-84.
[3] FRIZZI S,KAABI R,BOUCHOUICHA M,et al.Convolutional neural network for video fire and smoke detection[C]//IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.Piscataway:IEEE,2016:877-882.
[4] SHARMA J,GRANMO O C,GOODWIN M,et al.Deep convolutional neural networks for fire detection in images[J].Engineering applications of neural networks,2017,744:183-193.
[5] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations. San Diego,USA:ICLR,2015:1-14.
[6] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE,2016:770-778.
[7] 林作永,谌瑶.基于深度卷积神经网络的火灾预警算法研究[J].信息通信,2018(5):38-42.
[8] SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE,2016:2818-2826.
[9] HOWARD A G,ZHU M L,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].(2017-04-17)[2020-12-01].https://arxiv.org/pdf/1704.04861.pdf.
[10] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(6):1137-1149.
[11] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot MultiBox detector[M]//Computer Vision-ECCV 2016.Cham:Springer International Publishing,2016:21-37.
[12] DAI J F,LI Y,HE K M,et al.R-FCN:object detection via region-based fully convolutional networks[C]//NIPS′16:Proceedings of the 30th International Conference on Neural Information Processing Systems.Barcelona,Spain:NIPS,2016:379-387.
[13] IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//32nd International Conference on Machine Learning 2015.Lille,France:ICML,2015:448-456.
[14] 夏雪,袁非牛,章琳,等.从传统到深度:视觉烟雾识别、检测与分割[J].中国图象图形学报,2019,24(10):1627-1647.
[15] LIN T Y,DOLLwidth=11,height=14,dpi=110R P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE,2017:936-944.
[16] SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE,2015:1-9.
[17] SZEGEDY C，IOFFE S，VANHOUCKE V，et al. Inception-v4，Inception-ResNet and the impact of residual connections on learning[EB/OL].(2016-08-23)[2020-12-01].https://arxiv.org/pdf/1602.07261.pdf.
[18] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2018:7132-7141.
[19] REDMON J，FARHADI A.YOLOv3:an incremental improvement[EB/OL].(2018-04-08)[2020-12-01]. https://arxiv.org/pdf/1804.02767.pdf.

相似文献/References:

[1]赵俊杰,王金伟.基于SmsGAN的对抗样本修复[J].郑州大学学报(工学版),2021,42(01):50.[doi:10.13705/j.issn.1671-6833.2021.01.008]
　ZHAO Junjie,WANG Jinwei,Recovery of Adversarial Examples Based on SmsGAN[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):50.[doi:10.13705/j.issn.1671-6833.2021.01.008]
[2]薛均晓,武雪程,王世豪,等.基于改进YOLOv4的自然人群口罩佩戴检测研究[J].郑州大学学报(工学版),2022,43(04):16.[doi:10.13705/j.issn.1671-6833.2022.04.020]
　XUE Junxiao,WU Xuecheng,WANG Shihao,et al.A Method on Mask Wearing Detection of Natural Population Based on Improved YOLOv4[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):16.[doi:10.13705/j.issn.1671-6833.2022.04.020]

更新日期/Last Update: 2021-10-11