基于FSSD的微光烟雾检测方法

Low light level smoke detection method based on FSSD

针对目前现有的烟雾检测方法大多只适用于光照充足的环境,在微光环境下检测效果较差的问题,提出一种基于FSSD的微光烟雾检测方法。首先基于单高斯建模方法实现对包含运动目标的视频帧提取;其次,由于微光烟雾图像具有的低对比度、低信噪比等特性会对目标检测造成困难,设计了对比度受限自适应直方图均衡算法和中值滤波结合的图像预处理方法;最后为加强烟雾早期预警能力,采用了有利于检测小目标的FSSD网络并在网络输出端嵌入注意力机制模块,加强了关键的特征信息。在微光烟雾数据集上提出方法的Recall, Precision, F1分别达到了97.5%,93.3%和95.4%,表明该方法是可行有效的,可以应用于微光环境下的烟雾检测。

Aiming at the problem that most of the existing smoke detection methods are only suitable for the environment with sufficient light and the detection effect is poor in the low light environment, this paper proposes a low light smoke detection method based on FSSD. Firstly, the video frames containing moving objects are extracted based on single gaussian modeling method;secondly, the low contrast and low signal-to-noise ratio characteristics of low light level smoke image will cause difficulties in target detection, so the image preprocessing method combining the contrast limited adaptive histgram equalization and median filter is designed. Finally, in order to strengthen the early warning ability, the FSSD network that is conducive to detecting small targets is adopted, convolutional block attention module is embedded in the output of the network, which can strengthen the important feature information and improves the accuracy of target detection. On the low light smoke dataset, the Recall, Precision, and F1 of the proposed method reached 97.5%, 93.3% and 95.4%, indicating that the method is effective and can be applied to smoke detection in low light environments.