摘要
针对传统火焰检测模型的检测准确度较低和速度慢等问题,提出一种优化的卷积神经网络和超像素分割算法的视频火焰区域检测方法。首先使用火焰图像数据集对模型进行训练和验证,采用卷积核堆叠替换的方法改进Inception模块的结构;其次采用小卷积核替换的方法改进网络的前端结构,并将Focal-Loss函数作为损失函数以提高模型的泛化能力;然后设计InceptionV1模型的参数复杂度优化实验,生成优化的火焰检测网络结构;最后将超像素分割算法提取的火焰超像素语义信息输入优化的InceptionV1模型中,并进一步执行视频火焰区域的定位检测。实验结果表明,所提方法能够增强视频火焰的非线性特征提取能力,火焰检测准确度高于96%,检测速度较原始模型提升2.66倍。
To address the low detection accuracy and slow speed of traditional flame detection model,a video flame region detection method based on optimal convolutional neural network and hyperpixel segmentation algorithm is proposed.First,the flame image dataset is used to train and verify the model,and the structure of the Inception module is improved by stacking and replacing the convolution kernel.Second,the small convolution kernel replacement is adopted to improve the front-end structure of the network,and the Focal-Loss function is used as the loss function to improve the generalization ability of the model.Next,the parameter complexity optimization experiment of the InceptionV1 model is designed to generate an optimized flame detection network structure.Finally,the flame super-pixel semantic information extracted by the superpixel segmentation algorithm is input into the optimized InceptionV1 model,and the location detection of the video flame area is further performed.Experimental results show that the proposed method can enhance the nonlinear feature extraction of video flames;the accuracy of flame detection is higher than 96%,and the detection speed is 2.66 times that of the original model.
作者
邓军
姚涵文
王伟峰
李钊
梁策
Deng Jun;Yao Hanwen;Wang Weifeng;Li Zhao;Liang Ce(School of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi'an,Shaanxi 710054,China;School of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an,Shaanxi 710054,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第2期68-77,共10页
Laser & Optoelectronics Progress
基金
陕西省重点研发计划(2017ZDCXL-GY-01-02-03,2017ZDXM-SF-092)
西安科技大学优秀青年科技基金(2019Q2-01)。
关键词
图像处理
火焰检测
卷积神经网络
卷积核堆叠替换
参数复杂度优化
超像素定位
image processing
fire detection
convolutional neural network
convolution kernel stack replacement
parameter complexity optimization
super-pixel localization
