一种基于改进的MobileNetV2网络语义分割算法

Semantic Segmentation Algorithm Based on Improved MobileNetV2

基于金字塔卷积神经网络的语义分割算法准确率很高,但是其计算资源消耗巨大、算法执行时间长、无法满足实时性要求.为了解决这个问题,本文做出了以下改进:(1)用MobileNet替换原网络的结构,减少了网络运算时间和内存开销;(2)引入编码器-解码器结构提高输出图像的分辨率,进一步细化分割结果;(3)针对高分辨率图像推断时间过长的问题,本文设计了多级图像输入方法,降低了网络推断高分辨率图像所消耗的时间.本文在VOC 2012数据集和Cityscapes数据集上进行了测试,并与FCN、SegNet、DeepLab、PSPNet以及DFN等语义分割模型对比.实验结果表明,本文设计的语义分割算法在VOC 2012数据集上达到了76.1%的mIoU,在Cityscapes数据集上达到了74.1%的mIoU,略低于传统语义分割算法;处理一张分辨率为1024×512的图片需要18ms,少于传统语义分割算法,满足了实时性要求,达到了准确率与计算资源消耗之间的平衡.

The algorithm of semantic segmentation based on pyramid convolution neural network has high accuracy,but it consumes a lot of computing resources,takes a long time to execute,and cannot meet the real-time requirements.To overcome these shortcomings,this paper made the following improvements:(1)replacing the original network structure with MobileNet in order to reduce the computation time and memory consumption;(2)using encoder-decoder structure to improve the resolution of the output image and further refine the segmentation results;(3)using a multi-level image input method,which can reduce the time consumed by network inference of high-resolution image.Our method was tested on the VOC 2012 dataset and the Cityscapes dataset compared with other state-of-the-art semantic segmentation models such as FCN(Fully Convolutional Networks),SegNet,DeepLab,PSPNet and DFN(Discriminative Feature Network).Experimental results showed that our method achieved mIoU of 76.1%on the VOC 2012 dataset,and achieved mIOU of 74.1%on the Cityscapes dataset,which was a little lower than the traditional semantic segmentation algorithms.It took 18ms for our method to predict a 1024×512 picture,which achieved a balance between accuracy and computational resource consumption.