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Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation

机译：Squeeze-SegNet：用于语义分割的新型快速深层卷积神经网络

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The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.
机译：深度卷积神经网络的最新研究将注意力集中在提高准确性方面，这提供了重大进展。但是，如果它们仅限于分类任务，则在当今从事这一领域的科学共同体的贡献下，它们已在诸如对象检测和逐像素语义分割等更高级别的任务中变得非常有用。因此，在具有深度学习的语义分段领域中，出色的构想已经完成了准确性的发展，然而，这种结构变得很难像自动驾驶那样应用于嵌入式系统中。我们提出了一种新的深度完全卷积神经网络，用于逐像素语义分割，我们将其称为Squeeze-SegNet。该体系结构基于Encoder-Decoder样式。我们使用类似SqueezeNet的编码器和由我们建议的squeeze-decoder模块和upsample层（使用SegNet中的下采样索引）组成的解码器，并添加了反卷积层以提供最终的多通道特征图。在像Camvid或City-states这样的数据集上，我们的网络获得SegNet级别的准确性，其参数比SegNet少10倍。

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