基于深度神经网络的移动端人像分割

针对现有的人像分割算法大多忽略移动设备的硬件限制，盲目追求效果，以致无法满足移动端对于分割速度要求的问题，提出了一种可在移动设备上高效运行的人像分割网络。首先，基于编码器-解码器的轻量级U型架构来构建网络；其次，为了克服全卷积网络（FCN）受制于较小的感受域，无法充分捕获长距离信息的缺陷，引入期望最大化注意力块（EMAU）置于编码器之后、解码器之前；然后，在训练阶段添加多层边界辅助损失，有助于提高人物边界轮廓的准确度；最后，对模型进行量化和压缩。在Veer数据集上将所提网络与PortraitFCN+、ENet和BiSeNet等网络进行对比实验。实验结果表明，所提网络可以提高图像推理速度和分割效果，并能够以95.57%的准确率处理分辨率为224×224的RGB图像。

Portrait segmentation on mobile devices based on deep neural network

Most of the existing portrait segmentation algorithms ignore the hardware limitation of mobile devices and blindly pursue the effect, so that they cannot meet the segmentation speed requirement of mobile terminals. Therefore, a portrait segmentation network which could run efficiently on mobile devices was proposed. Firstly, the network was constructed based on the lightweight U-shaped architecture of encoder-decoder. Secondly, in order to make up for the fact that the Fully Convolutional Network (FCN) was limited by a small sensing domain, so that it was not able to fully capture the long-distance information, an Expectation Maximization Attention Unit (EMAU) was introduced after the encoder and before the decoder. Thirdly, for improving the accuracy of portrait boundary contour, a multi-layer boundary auxiliary loss was added at the training stage. Finally, the model was quantized and compressed. The proposed network was compared with other networks such as PortraitFCN+, ENet and BiSeNet on Veer dataset. Experimental results show that, the proposed network can improve the image reasoning speed and segmentation effect, as well as process the RGB images with the resolution of 224×224 at the accuracy of 95.57%.