Texture-aware Network for Smoke Density Estimation

Smoke density estimation, also termed as soft segmentation, was developed from pixel-wise smoke (hard) segmen-tation and it aims at providing transparency and segmentation confidence for each pixel. The key difference between them lies in that segmentation focuses on classifying pixels into smoke and non-smoke ones, while density estimation obtains inner transparency of smoke component rather than treat all smoke pixels as an equal value. Based on this, we propose a texture-aware network being able to capture inner transparency of smoke components rather than merely focus on general smoke distribution for pixel-wise smoke density estimation. Besides, we adapt the Squeeze-and-Excitation (SE) layer for smoke feature extraction by involving max values for robustness. In order to represent inhomogeneous smoke pixels, we proposed a simple yet efficient attention-based texture-aware module that involves both gradient and semantic information. Experimental results show that our method outperforms others in both single image density estimation or segmentation and video smoke detection.