NDLI: Fully Convolutional Pyramidal Networks for Semantic Segmentation

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Fengxiao Li Zourong Long Peng He Peng Feng Xiaodong Guo Xuezhi Ren Biao Wei Mingfu Zhao Bin Tang
Abstract	Semantic segmentation networks focus on the scene parsing of an unrestricted open scene. The typical segmentation architectures are stacks consisting of convolutional layers, which are used to extract semantic features. The feature map dimension is sharply changed at sampling units for most of networks, which ensure effective propagation of the gradient in deep nets. In this article, we proposed a state-of-the-art network model named Fully Convolutional Pyramidal Networks (FC-PRNet), which employs pyramidal residual structure to change the feature map dimension at all convolutional layers. This design is an effective way of improving generalization ability and optimizing parameters, and FC-PRNet could achieve excellent capability of semantic extraction. We used urban scene benchmark CamVid and KITTI dataset to test our network, the experimental results show that FC-PRNet achieves better results without any pre-training or post-treatment module. Moreover, due to smart construction of pyramidal residual structures, FC-PRNet has less parameters than other existing networks trained on these datasets.
e-ISSN	21693536
DOI	10.1109/ACCESS.2020.3045280
Journal	IEEE Access
Volume Number	8
Language	English
Publisher	IEEE
Publisher Date	2020-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Semantic Segmentation Artificial Intelligence Lightweight Model Kiiti Data Sets
Content Type	Text
Resource Type	Article

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