Abstract
With the development of deep learning, semantic segmentation has received considerable attention within the robotics community. For semantic segmentation to be applied to mobile robots or autonomous vehicles, real-time processing is essential. In this article, a new real-time semantic segmentation network, called the adjacent feature propagation network (AFPNet), is proposed to achieve high performance and fast inference. AFPNet executes in real time on a commercial embedded GPU. The network includes two new modules. The local memory module (LMM) is the first; it improves the upsampling accuracy by propagating the high-level features to the adjacent grids. The cascaded pyramid pooling module (CPPM) is the second; it reduces computational time by changing the structure of the pyramid pooling module. Using these two modules, the proposed AFPNet achieved 76.4% mean intersection-over-union on the Cityscapes test dataset, outperforming other real-time semantic segmentation networks. Furthermore, AFPNet was successfully deployed on an embedded board Jetson AGX Xavier and applied to the real-world navigation of a mobile robot, proving that AFPNet can be effectively used in a variety of real-time applications.
Original language | English |
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Pages (from-to) | 5877-5888 |
Number of pages | 12 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Volume | 52 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2022 Sep 1 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering