Real-time Image Semantic Segmentation Based on Block Adaptive Feature Fusion

被引：0

作者：

Huang T.-H. ^{[1
]}

Nie Z.-Y. ^{[1
]}

Wang Q.-G. ^{[2
]}

Li S. ^{[3
]}

Yan L.-C. ^{[1
]}

Guo D.-S. ^{[1
]}

机构：

[1] College of Information Science and Engineering, National Huaqiao University, Xiamen

[2] Institute for Intelligent Systems, University of Johannesburg, Johannesburg

[3] the Hong Kong Polytechnic University, Hong Kong

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Block adaptive feature fusion (BAFF); Deep learning; Real-time semantic segmentation network; SkipNet;

D O I：

10.16383/j.aas.c180645

中图分类号：

学科分类号：

摘要：

Recently, image semantic segmentation has made great progress with deep learning, which benefits robotics and automatic driving vehicle. This paper proposes a real-time semantic segmentation algorithm based on block adaptive feature fusion (BAFF). Under the framework of a light convolutional network, a block adaptive feature fusion algorithm is proposed in the context-embedding module, to improve the accuracy of real-time semantic segmentation. First, the problem caused by the different size of receptive field in layers is analyzed, and a feature fusion mechanism with block weight is presented on SkipNet. Then, layers' feature integration is carried on by three-dimension convolution. The feature- weights are calculated by an additional network with depthwise-separable-convolutions (DSC). Finally, the features are fused under adaptive weights. Experiments show that this method obtains excellent segmentation results with a good balance between rapidity and accuracy and owns robustness on segmentation of complex scenes. Copyright © 2021 Acta Automatica Sinica. All rights reserved.

引用

页码：1137 / 1148

页数：11

共 30 条

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