BSDNet: Balanced Sample Distribution Network for Real-Time Semantic Segmentation of Road Scenes
In recent years, semantic segmentation based on deep convolutional neural networks has developed rapidly. However, it is still a challenge to balance the computing cost and segmentation performance for the current semantic segmentation methods. This paper proposes a lightweight real-time semantic se...
Main Authors: | Lv Ye, Jianxu Zeng, Yue Yang, Ashara Emmanuel Chimaobi, Nyaradzo Mercy Sekenya |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9448235/ |
Similar Items
-
Global Multi-Attention UResNeXt for Semantic Segmentation of High-Resolution Remote Sensing Images
by: Zhong Chen, et al.
Published: (2023-03-01) -
RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images
by: Runrui Liu, et al.
Published: (2022-06-01) -
LA-ShuffleNet: A Strong Convolutional Neural Network for Edge Computing Devices
by: Hui Zhang, et al.
Published: (2023-01-01) -
MFSNet: Enhancing Semantic Segmentation of Urban Scenes with a Multi-Scale Feature Shuffle Network
by: Xiaohong Qian, et al.
Published: (2023-12-01) -
Bilateral U‐Net semantic segmentation with spatial attention mechanism
by: Guangzhe Zhao, et al.
Published: (2023-06-01)