关键词: 语义分割, 特征融合, 全卷积神经网络, 空洞卷积

Abstract:

At present, deep full convolutional networks have made considerable achievements in the field of image semantic segmentation. However, with the repeated down-sampling operation, the detail information of the feature map is greatly lost, which affects the segmentation accuracy. In this paper, it designs a deep full convolutional network for semantic segmentation. The network structure consists of encoder and decoder. Some dilated convolution layers are introduced at the end of the encoder to reduce the loss of detailed information. In each stage of the decoding process, the low-order semantic features of corresponding dimensions are integrated, and the global features are integrated at the end of decoder to improve the segmentation accuracy. Finally, it uses the data-enhanced CamVid dataset to train and test the network. And the test results reach 90.14% PA and 71.95% mIoU. Experimental results show that the output of the network performs better in terms of regional smoothing.

Key words: semantic segmentation, feature fusion, full convolutional neural network, dilated convolution