  Smoke segmentation is essential to precisely localize wildfire so that it can
be extinguished in an early phase. Although deep neural networks have achieved
promising results on image segmentation tasks, they are prone to be
overconfident for smoke segmentation due to its non-rigid shape and transparent
appearance. This is caused by both knowledge level uncertainty due to limited
training data for accurate smoke segmentation and labeling level uncertainty
representing the difficulty in labeling ground-truth. To effectively model the
two types of uncertainty, we introduce a Bayesian generative model to
simultaneously estimate the posterior distribution of model parameters and its
predictions. Further, smoke images suffer from low contrast and ambiguity,
inspired by physics-based image dehazing methods, we design a
transmission-guided local coherence loss to guide the network to learn
pair-wise relationships based on pixel distance and the transmission feature.
To promote the development of this field, we also contribute a high-quality
smoke segmentation dataset, SMOKE5K, consisting of 1,400 real and 4,000
synthetic images with pixel-wise annotation. Experimental results on benchmark
testing datasets illustrate that our model achieves both accurate predictions
and reliable uncertainty maps representing model ignorance about its
prediction. Our code and dataset are publicly available at:
https://github.com/redlessme/Transmission-BVM.
