It is difficult to detect forest fires in complex backgrounds owing to the many interfering factors in forest fire smoke. In this paper, a novel method that combines Time Domain Robust Principal Component Analysis (TRPCA) and a Two-Stream Composed of Visual Geometry Group Network (VGG) and Bi-Long Short-Term Memory (BLSTM) (TSVB) model is proposed for forest fire smoke detection. First, features are extracted from the smoke video from the spatial stream (static) and time stream (dynamic). For the spatial stream, static features are extracted from a single-frame image of the smoke video using the VGG network. For the time stream, continuous-frame binary images of the smoke are obtained using the TRPCA algorithm. Then, the dynamic features of the smoke are extracted by VGG and BLSTM. Finally, the static and dynamic features are fused using a concatenate function to achieve forest fire smoke detection. The experimental results show that compared with the single-feature model, the proposed method effectively improves learning ability and prediction ability, and shows strong robustness against interference factors in a complex background, with accuracy of forest fire smoke detection reaching 90.6%.

由于森林火灾烟雾中的许多干扰因素，很难在复杂的背景下检测到森林火灾。本文提出了一种结合时域鲁棒主成分分析（TRPCA）和视觉几何群网络（VGG）和双向长期短期记忆（BLSTM）模型（TSVB）的两流组合的新方法。森林火灾烟雾探测。首先，从空间流（静态）和时间流（动态）的烟雾视频中提取特征。对于空间流，使用VGG网络从烟雾视频的单帧图像中提取静态特征。对于时间流，使用TRPCA算法获得烟雾的连续帧二进制图像。然后，通过VGG和BLSTM提取烟雾的动态特征。最后，使用连接功能将静态和动态特征融合在一起，以实现森林火灾烟雾的检测。实验结果表明，与单特征模型相比，该方法有效地提高了学习能力和预测能力，在复杂背景下对干扰因素具有较强的鲁棒性，森林火灾烟雾探测精度达到90.6％。