关键词: 3D残差卷积网络, 烟雾检测, 注意力机制, 深度学习

Abstract:

Most existing video smoke detection methods identify candidate smoke image patches by motion detection, and manually extract combined features of smoke, which accuracy is difficult to meet requirements in complex scenes. Aiming at the above problems, a video smoke detection method based on a spatiotemporal dual path 3D residual convolutional network is proposed. The candidate smoke regions are extracted based on the wavelet low-frequency component difference between the mixed Gaussian background model and the original video frame. A spatiotemporal dual path 3D residual convolutional network is constructed, and the attention module is introduced to the weighted fusion of spatial and temporal features of smoke to achieve end-to-end smoke recognition. The experimental results show that this method can get a more complete candidate smoke regions, especially for the smoke which is too thin or thick, and compared with the traditional smoke detection method and 2D smoke detection convolutional network, smoke detection accuracy has been improved.

Key words: 3D residual convolutional network, smoke detection, attention module, deep learning