Fully Convolutional Neural Network with Attention Module for Semantic Segmentation

Abstract

A fully convolutional neural network is a powerful end-to-end model that is widely used in the field of semantic segmentation and has achieved great success. Researchers have proposed a series of methods based on a fully convolutional neural network. However, with the continuous subsampling of convolutions and pooling, the image contextual information will be lost, affecting the pixel-level classification. To solve the problem of context loss in a fully convolutional network, a pixel-based attention method is proposed, which calculates the relationship bet-ween high-level feature map pixels to obtain global information and enhance the correlation between pixels com-bined with atrous spatial pyramid pooling to further extract the image feature information. To solve the problem of pixel loss in the high-level feature map of an image, an attention method based on different levels of the image is proposed. This method uses the information in the high-level feature map as a guide to mine the hidden information in the low-level feature map and then fuses it with the high-level feature map to make full use of the high-level feature map and the low-level feature map information. In the experiment, the effectiveness of the proposed method is verified by comparing the effects of different modules on the segmentation results of a fully convolutional neural network. At the same time, experiments are carried out on the recognized image semantic segmentation dataset called Cityscapes and compared with the current advanced networks. The results show that the proposed method has advantages in both objective evaluation indicators and subjective effects, and achieves 69.3% accuracy in the Cityscapes official website test set. The performance is 3 to 5 percentage points higher than that of several recent advanced networks.

Keywords

• |fully convolutional neural network|atrous spatial pyramid pooling|attention module|semantic segmentation