Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.

中文翻译：

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通过改进的MobileNet模型在浅层中进行局部接收场扩展的新图像分类方法。

由于深度神经网络（DNN）既需要大量内存，又需要大量计算，因此很难将其应用于硬件资源有限的嵌入式系统。因此，需要压缩和加速DNN模型。通过应用深度可分离卷积，MobileNet可以减少参数数量和计算复杂度，而分类精度的损失则更少。在MobileNet的基础上，提出了3种改进的在浅层中具有局部接收场扩展的MobileNet模型，也称为Dilated-MobileNet（扩散卷积MobileNet）模型，其中将膨胀卷积引入到MobileNet模型的特定卷积层中。在不增加参数数量的情况下，扩张卷积用于增加卷积滤波器的接收场，以获得更好的分类精度。实验分别在具有属性数据集的Caltech-101，Caltech-256和Tubingen动物上进行。结果表明，与MobileNet相比，Dilated-MobileNets可获得高达2％的分类精度。