关键词: 语义分割, 卷积神经网络, 机器视觉, 密集预测, 全连接条件随机场

Abstract:

Semantic segmentation is a kind of pixel level segmentation and classification of different objects in images, it is important for image processing and is widely used in different fields. Deep convolutional neural networks has achieved remarkable results in machine vision in recent years. Aiming at the task of semantic segmentation of dense prediction, this paper proposes an improved method based on VGGNet in which the shallow information is fused with the deeper feature map, and feature extraction and fusion are carried out by using paralleling convolution with different sampling rates. This method is more effective in extracting features and context information from different layers, then improves the accuracy of semantic segmentation. This paper also improves the accuracy of semantic segmentation by optimizing the edge of the images with a fully connected conditional random fields. This paper reaches 71.3% mIOU in the semantic segmentation task of test set of PASCAL VOC 2012, and the result is superior to the previous main classical methods based on VGGNet.

Key words: semantic segmentation, convolutional neural networks, machine vision, dense prediction, fully connected conditional random fields