A Wave-shaped Deep Neural Network for Smoke Density Estimation
Feiniu Yuan, PhD, Professor, yfn@ustc.edu
Feiniu Yuan, Lin Zhang, Xue Xia, Qinghua Huang, and Xuelong Li, "A Wave-shaped Deep Neural Network for Smoke Density Estimation", IEEE Transactions on Image Processing, vol. 67, no. 11, 2020, pp. 9893-9903. |
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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. | |||
Three test data sets with ground truths: |
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DS01 with password "bymb", DS02 with password "0s0d", DS03 | ||
Three console testing programs with a trained W-Net model: |
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| Test programs and a trained W-Net model with password "oc9z". Please remember that our programs can process only jpg and png images. | |||
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