激光与光电子学进展, 2019, 56 (21): 211502, 网络出版: 2019-11-02
基于高斯混合模型和卷积神经网络的视频烟雾检测 下载: 907次
Video Smoke Detection Based on Gaussian Mixture Model and Convolutional Neural Network
机器视觉 视频烟雾检测 卷积神经网络 高斯混合模型 形态学 machine vision video smoke detection convolutional neural network Gaussian mixture model morphology
摘要
为满足复杂场景下视频烟雾检测的实时性、准确率等需求,提出了一种将高斯混合模型与卷积神经网络相结合的视频烟雾检测方法。基于高斯混合模型的背景减除法和形态学方法实现对视频图像的运动目标提取;针对烟雾检测效率和网络过拟合等问题,设计用于视频烟雾检测的卷积神经网络模型;通过烟雾正负样本图像对卷积神经网络进行训练和测试。在此基础上,合理地设定运动目标网络模型的输出概率的阈值,有效去除训练样本中没有涵盖的非烟雾干扰项,降低误报率。实验结果表明,该方法是可行且有效的,其视频烟雾检测准确率达到97.5%,平均烟雾报警响应时间为4.58 s,可满足复杂场景下烟雾的实时检测要求。
Abstract
This study proposes a video smoke detection method combining the Gaussian mixture model (GMM) with a convolutional neural network (CNN) to ensure real-time and accurate video smoke detection in complex scenarios. First, background subtraction based on GMM and morphological methods are used to extract motion objects from video images. Second, a CNN model for video smoke detection is designed, taking into account the limitations of smoke detection efficiency and overfitting of the network model. Finally, the designed CNN model is trained and tested by using positive and negative smoke sample images. The output probability threshold of the network model of motion objects is set reasonably, which can effectively remove the non-smoke interference items that are not covered in the training samples. The false alarm rate can thereby be reduced. Experimental results prove the validity and feasibility of the method. The accuracy of video smoke detection reaches 97.5%, and the average response time of the smoke alarm is 4.58 s, satisfying the real-time demand of smoke detection in complex scenarios.
李鹏, 张炎. 基于高斯混合模型和卷积神经网络的视频烟雾检测[J]. 激光与光电子学进展, 2019, 56(21): 211502. Peng Li, Yan Zhang. Video Smoke Detection Based on Gaussian Mixture Model and Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211502.