Convolutional Neural Network with Attention Mechanism for Image-Based Smoke Detection

Smoke detection based on computer vision plays a key role in fire prevention. With the development of deep learning, convolutional neural network frameworks are increasingly used in this field. However, due to the large span of smoke’s scale and the variety of its color as well as its texture, the features are even more complex. In addition, the sampling of features by the existing neural networks is limited to the size of the convolution kernel. It is difficult to extract the global features. So, detecting smoke from pictures is still a problem. In this work, we proposed a feature-fusion method based on local and global attention mechanisms. It is able to better fuse the low-level and high-level features. In order to solve the scale problem, we proposed a mechanism for selection and offset of the receptive field. In addition, we combine the transformer based on self-attention mechanism and convolution operation to make the model aware of global information. Finally, we optimize the sampling of feature maps by the detection head of the one-stage network based on the smoke color features. Experiments show that the proposed model outperforms state-of-the-art relevant competitors by 4.6% on AP.5:.95.