Inspired by the recent success of fully convolutional networks (FCN) in
semantic segmentation, we propose a deep smoke segmentation network to infer
high quality segmentation masks from blurry smoke images. To overcome large
variations in texture, color and shape of smoke appearance, we divide the
proposed network into a coarse path and a fine path. The first path is an
encoder-decoder FCN with skip structures, which extracts global context
information of smoke and accordingly generates a coarse segmentation mask. To
retain fine spatial details of smoke, the second path is also designed as an
encoder-decoder FCN with skip structures, but it is shallower than the first
path network. Finally, we propose a very small network containing only add,
convolution and activation layers to fuse the results of the two paths. Thus,
we can easily train the proposed network end to end for simultaneous
optimization of network parameters. To avoid the difficulty in manually
labelling fuzzy smoke objects, we propose a method to generate synthetic smoke
images. According to results of our deep segmentation method, we can easily and
accurately perform smoke detection from videos. Experiments on three synthetic
smoke datasets and a realistic smoke dataset show that our method achieves much
better performance than state-of-the-art segmentation algorithms based on FCNs.
Test results of our method on videos are also appealing.

本研究提出了一种深度烟雾分割网络，通过使用全卷积网络（FCN）、编码器 - 解码器 FCN、融合添加卷积激活层等多种技术手段，可以高效、准确的从模糊的烟雾图像中分割出高质量的 MASK，实现了对烟雾进行精确定位及检测。