Abstract:
Smoke density estimation from a single image is a novel, ill-posed problem. We propose a wave-shaped neural network, W-Net, by stacking convolutional encoder-decoder structures.
Stacking encoder-decoders directly increases network depth, expanding receptive fields for semantic information encoding. To maximize feature re-usage, we copy and resize encoding layer outputs to decoding layers and concatenate them to implement short-cut connections for spatial accuracy.
We also use short-cut connections between W-Net’s crests and troughs and decoding layers. Smoke segmentation, detection, and disaster simulation use estimated smoke density.
Our smoke density estimation and segmentation method outperforms others in experiments. Visual auto exhaust detection is also successful.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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