Open AccessMulti‐stream densely connected network for semantic segmentation
Author(s) -
Jia Dayu,
Cao Jiale,
Pan Jing,
Pang Yanwei
Publication year - 2022
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/cvi2.12078
Subject(s) - computer science , pascal (unit) , segmentation , artificial intelligence , pyramid (geometry) , fuse (electrical) , feature (linguistics) , pattern recognition (psychology) , context (archaeology) , scale (ratio) , feature extraction , mathematics , cartography , paleontology , linguistics , philosophy , geometry , geography , electrical engineering , biology , programming language , engineering
Semantic segmentation is a challenging task in computer vision which is widely used in autonomous driving and scene understanding. State‐of‐the‐art semantic segmentation networks, like DeepLab and PSPNet, make full use of multiple feature information to improve spatial resolution. However, the feature resolution in the scale‐axis is not dense enough for practical applications. To tackle this problem, a multi‐stream network is designed with atrous convolutional layers at multiple rates to capture objects and context at multiple scales. Furthermore, intra‐connections and inter‐connections are designed to fuse multi‐scale features densely which produce a feature pyramid with much larger scale diversity and larger receptive field by involving small quantity of computation. The proposed module can be easily used in other methods and it helps to increase the performance. Compared with existing methods, the proposed network, called Multi‐stream Densely Connected Network, reaches competitive results on ADE20K dataset, PASCAL VOC 2012 dataset, and Cityscapes dataset.