A Wave-shaped Deep Neural Network for Smoke Density Estimation.

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ID: 61664
2019
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Ranked #24 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Smoke density estimation from a single image is a totally new but highly ill-posed problem. To solve the problem, we stack several convolutional encoder-decoder structures together to propose a wave-shaped neural network, termed W-Net. Stacking encoder-decoders directly increases the network depth, leading to the enlargement of receptive fields for encoding more semantic information. To maximize the degrees of feature re-usage, we copy and resize the outputs of encoding layers to corresponding decoding layers, and then concatenate them to implement short-cut connections for improving spatial accuracy. The crests and troughs of W-Net are special structures containing abundant localization and semantic information, so we also use short-cut connections between these structures and decoding layers. Estimated smoke density is useful in many applications, such as smoke segmentation, smoke detection, disaster simulation. Experimental results show that our method outperforms existing methods on both smoke density estimation and segmentation. It also achieves satisfying results in visual detection of auto exhausts.
Reference Key
yuan2019aieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuan, Feiniu;Zhang, Lin;Xia, Xue;Huang, Qinghua;Li, Xuelong;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2019
DOI
10.1109/TIP.2019.2946126
URL
Keywords
dust hearing loss world trade center disaster

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