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- W4316660778 abstract "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." @default.
- W4316660778 created "2023-01-17" @default.
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- W4316660778 date "2022-12-13" @default.
- W4316660778 modified "2023-10-16" @default.
- W4316660778 title "Texture-aware Network for Smoke Density Estimation" @default.
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- W4316660778 doi "https://doi.org/10.1109/vcip56404.2022.10008826" @default.
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