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.