[1]龚成张,严云洋,卞苏阳,等.基于Fast-CAANet的火焰检测方法[J].南京师大学报(自然科学版),2024,(02):109-116.[doi:10.3969/j.issn.1001-4616.2024.02.013]
 Gong Chengzhang,Yan Yunyang,Bian Suyang,et al.Flame Detection Based on Fast-CAANet[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(02):109-116.[doi:10.3969/j.issn.1001-4616.2024.02.013]
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基于Fast-CAANet的火焰检测方法()
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《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

卷:
期数:
2024年02期
页码:
109-116
栏目:
计算机科学与技术
出版日期:
2024-06-15

文章信息/Info

Title:
Flame Detection Based on Fast-CAANet
文章编号:
1001-4616(2024)02-0109-08
作者:
龚成张1严云洋12卞苏阳1祝巧巧1冷志超1
(1.淮阴工学院计算机与软件工程学院,江苏 淮安 223003)
(2.江苏海洋大学计算机工程学院,江苏 连云港 222005)
Author(s):
Gong Chengzhang1Yan Yunyang12Bian Suyang1Zhu Qiaoqiao1Leng Zhichao1
(1.Faculty of Computer & Software Engineering,Huaiyin Institute of Technology,Huaian 223003,China)
(2.School of Computer Engineering,Jiangsu Ocean University,Lianyungang 222005,China)
关键词:
深度学习特征提取注意力机制火焰检测
Keywords:
deep learningfeature extractionattention mechanismflame detection
分类号:
TP391.44
DOI:
10.3969/j.issn.1001-4616.2024.02.013
文献标志码:
A
摘要:
高效率高速度的火焰检测方法对预防火灾、保护社会安全具有十分重要的作用. 本文面向社会安全应用需求,提出一种基于Fast-CAANet的火焰检测方法. 先提出一种CAA模块,加强卷积和注意力机制的有效融合; 然后构建CAANet网络的主干网络(CAABlock),更有效提取火焰的丰富特征; 再提出参数更小、准确度更高的Fast-CAABlock模块,提出了加强火焰特征提取的方案. 实验结果表明,Fast-CAANet准确率达到91.42%,计算量3.9 GMac较小. 所提火焰检测算法与其它算法相比,性能更优,效果更好.
Abstract:
High efficiency and high speed flame detection plays an important role in preventing fire and protecting social security. In this paper,a flame detection method based on Fast-CAANet is proposed to meet the needs of social security applications. Firstly,a CAA module is proposed to strengthen the effective integration of convolution and attention mechanism. Then the main network of CAANet(CAABlock)is constructed to extract the rich characteristics of flame more effectively. The Fast-CAABlock module with smaller parameters and higher accuracy is proposed to enhance the flame feature extraction scheme. Experimental results show that the accuracy of Fast-CAANet is up to 91.42%,and the calculation amount is 3.9 GMac. Compared with other algorithms,the proposed flame detection algorithm has better performance and effect.

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备注/Memo

备注/Memo:
收稿日期:2023-06-16.
基金项目:国家自然科学基金项目(61402192)、江苏省“六大人才高峰”项目(2013DZXX-023)、江苏省“青蓝工程”、淮安市“533 英才工程”项目.
通讯作者:严云洋,博士,教授,研究方向:数字图像处理、模式识别. E-mail:yunyang@hyit.edu.cn
更新日期/Last Update: 2024-06-15