嵌入通道注意力的YOLOv4火灾烟雾检测模型

谢书翰; 张文柱; 程鹏; 杨子轩

doi:10.37188/CJLCD.2020-0312

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嵌入通道注意力的YOLOv4火灾烟雾检测模型

图像处理 | 更新时间：2021-09-30

- 嵌入通道注意力的YOLOv4火灾烟雾检测模型
- Firesmoke detection model based on YOLOv4 with channel attention
- 液晶与显示 2021年36卷第10期页码：1445-1453
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院, 陕西西安 710048
- 作者简介：
  
  [ "谢书翰(1996-), 男, 江西赣州人, 硕士, 2021年于西安建筑大学获得硕士学位, 主要从事模式识别方面的研究。E-mail:876851890@qq.com" ]
  [ "张文柱(1970-), 男, 辽宁葫芦岛人, 博士, 教授, 2004年于西安电子科技大学获得博士学位, 主要从事无线通信网络、移动边缘计算、模式识别等方面的研究。E-mail: wzzhang@xauat.edu.cn" ]
- 基金信息：
  
  陕西省自然科学基础研究计划资助项目(2020JM-489);西安市科技计划项目(JZKD0010);陕西省教育厅重点项目高端智库(18JT006)
- DOI：10.37188/CJLCD.2020-0312
  中图分类号： TP391.4
- 收稿日期：2020-11-20，
  
  修回日期：2021-03-02，
  
  纸质出版日期：2021-10
- 稿件说明：
移动端阅览
谢书翰, 张文柱, 程鹏, 等. 嵌入通道注意力的YOLOv4火灾烟雾检测模型[J]. 液晶与显示, 2021,36(10):1445-1453.

Shu-han XIE, Wen-zhu ZHANG, Peng CHEN, et al. Firesmoke detection model based on YOLOv4 with channel attention[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1445-1453.
谢书翰, 张文柱, 程鹏, 等. 嵌入通道注意力的YOLOv4火灾烟雾检测模型[J]. 液晶与显示, 2021,36(10):1445-1453. DOI： 10.37188/CJLCD.2020-0312.

Shu-han XIE, Wen-zhu ZHANG, Peng CHEN, et al. Firesmoke detection model based on YOLOv4 with channel attention[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1445-1453. DOI： 10.37188/CJLCD.2020-0312.

摘要

为提高火灾烟雾检测模型的性能，以及避免繁琐的手工烟雾特征提取过程，本文提出一种基于卷积神经网络YOLOv4的火灾烟雾检测模型。该方法以CSPDarkNet53为主干网络，在主干网络的最后一层加入了13×13.9×9.5×5.1×1四个不同尺度的最大池化，多尺度特征融合中采用了PANet（Path Aggregation Network）以提高网络特征提取能力；为了增强网络预测头提取有效烟雾信息的能力，在网络预测头加入通道注意力网络。针对火灾烟雾数据集，候选框的尺寸使用K-means进行聚类以得到更加符合贴近火灾烟雾数据集的尺寸；由于本文仅识别火灾烟雾一种类别，所以精简损失函数，剔除分类误差，使算法收敛得更快。训练阶段使用了图像翻转、放缩和随机擦除等数据增强方法以降低过拟合的风险。实验结果表明，所提出的火灾烟雾检测模型精度高，其准确率达到92.5%，召回率达到87.7%，同时检测速度可达5.1帧/s，提高了火灾烟雾检测模型的性能。

Abstract

To improve the precision and recall rate of fire smoke detection model in multi-scene fire smoke detection applications

and avoid the tedious manual smoke feature extraction process

a fire smoke detection model is proposed which is based on convolutional neural network YOLOv4. In the last layer of the backbone network

four different scales of maximum pooling are added: 13×13

9×9

5×5

and 1×1. The multi-scale feature fusion uses PANet (Path Aggregation Network) to improve network feature extraction capabilities. In addition

a channel attention network is added to the network prediction head to enhance the ability of the YOLO Head to extract effective smoke information. For the fire smoke data set

the size of the candidate frame is clustered using the K-means algorithm to get a size closer to the fire smoke data set. Due to the identification of smoke

the loss function is simplified

the classification error is eliminated

and the algorithm converges faster. Data enhancement methods such as image flipping and random erasure are used in the training phase to reduce the risk of overfitting. Experimental results show that the fire smoke detection model has excellent performance. Its precision can reach 92.5%

Recall can reach 87.7%

and the detection speed can reach 5.1 frames/s

which improves the performance of fire smoke detection model in multi-scene fire smoke detection applications.

关键词

Keywords

references

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