改进的多尺度火焰检测方法

侯易呈; 王慧琴; 王可

doi:10.37188/CJLCD.2020-0221

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改进的多尺度火焰检测方法

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- 改进的多尺度火焰检测方法
- Improved multi-scale flame detection method
- 液晶与显示 2021年36卷第5期页码：751-759
- 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 710055
- 作者简介：
  
  [ "侯易呈(1996-), 男, 山西孝义人, 硕士研究生, 2018年于河南城建学院获得学士学位, 主要从事机器视觉及自动控制方面的研究。E-mail:819182588@qq.com" ]
  [ "王慧琴(1970-), 女, 山西长治人, 博士, 教授, 2002年于西安交通大学获得博士学位, 主要从事智能信息处理、信息理论与应用、信息技术与管理、数字建筑等研究。E-mail: hqwang@xauat.edu.cn" ]
- 基金信息：
  
  陕西省科技厅国际科技合作计划项目(2020KW-012);陕西省教育厅重点项目高端智库(18JT006);西安市科技局项目(GXYD10.1)
- DOI：10.37188/CJLCD.2020-0221
  中图分类号： TP394.1;TH691.9
- 收稿日期：2020-08-31，
  
  修回日期：2020-11-11，
  
  纸质出版日期：2021-05
- 稿件说明：
移动端阅览
侯易呈, 王慧琴, 王可. 改进的多尺度火焰检测方法[J]. 液晶与显示, 2021,36(5):751-759.

Yi-cheng HOU, Hui-qin WANG, Ke WANG. Improved multi-scale flame detection method[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 751-759.
侯易呈, 王慧琴, 王可. 改进的多尺度火焰检测方法[J]. 液晶与显示, 2021,36(5):751-759. DOI： 10.37188/CJLCD.2020-0221.

Yi-cheng HOU, Hui-qin WANG, Ke WANG. Improved multi-scale flame detection method[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 751-759. DOI： 10.37188/CJLCD.2020-0221.

摘要

网络层数的加深会造成对火焰目标深层特征细节信息表征能力减弱，同时提取了低相关度的冗余特征，导致火焰识别精度不高。针对该问题，提出了一种基于改进Faster R-CNN的火焰检测方法，以提高在深层网络下的火焰识别精度。首先利用ResNet50网络提取火焰特征，并添加SENet模块降低火焰目标冗余特征；然后将深层特征和浅层特征进行多尺度特征融合，增强深层特征的细节信息；最后训练网络，实现对火焰目标的识别定位。实验通过构建VOC火焰数据集进行网络训练，使用测试集进行检测，并进行特征图可视化对比，相比于改进前模型，本文模型平均精度提高了7.78%，召回率提高了9.05%，精确率提高了12.54%。本文提出的火焰目标检测模型，通过结合注意力机制模块和多尺度特征融合机制，能够有效进行火焰目标特征提取，火焰目标的检测结果更加准确。

Abstract

The deepening of the number of network layers can weaken the ability to characterize the detailed information of the deep features of the flame target

and at the same time extract redundant features with low correlation

resulting in low flame recognition accuracy. Aiming at this problem

a flame detection method based on improved Faster R-CNN is proposed to improve the accuracy of flame recognition in deep networks. Firstly

the ResNet50 network is used to extract flame features

and the SENet module is added to reduce the redundant features of flame targets. Then

the deep features and shallow features are multi-scale feature fusion to enhance the detailed information of deep features. Finally

the network is trained to realize the recognition of flame targets positioning. In the experiment

the VOC flame data set is constructed for network training

the test set is used for detection

and the feature map visualization is compared. Compared with the model before the improvement

the AP value increases by 7.78%

the recall increases by 9.05%

and the precision increases by 12.54%. By combining the attention mechanism module and the multi-scale feature fusion mechanism

the flame target detection model proposed in this paper

can effectively extract the flame target feature

and the flame target detection result is more accurate.

关键词

Keywords

references

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