NDLI: Neural network-based semantic segmentation model for robot perception of driverless vision

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Neural network-based semantic segmentation model for robot perception of driverless vision

Content Provider	IET Digital Library
Author	Ye, Lu Duan, Ting Zhu, Jiayi
Abstract	Driverless vision is one of the important applications of robot perception. With the development of driverless vehicles, the perception and understanding of the surrounding environment are becoming more and more important. When the types of surrounding objects are too complex, the ability of the computer to recognise the environment is poor. To improve the recognition accuracy of the computer and enhance the ability of segmentation, in this study, depth estimation is used to predict depth information to assist semantic segmentation, and then edge features of objects are introduced to enhance the contour of objects. A neural network-based semantic segmentation model is proposed. Finally, the intrinsic mechanism of attention is used to increase the correlation between channels. The experimental results on the CamVid data set show that this model can obtain better evaluation results and improve the segmentation accuracy of images compared with other models.
Starting Page	190
Ending Page	196
Page Count	7
Volume Number	2
e-ISSN	26316315
Issue Number	Issue 4, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-csr/2/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-csr.2020.0040
Journal	IET Cyber-Systems and Robotics
Publisher	The Institution of Engineering and Technology Zhejiang University Press
Publisher Date	2020-10-19
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Subject Keyword	Computer Vision And Image Processing Technique Driverless Vehicle Driverless Vision Feature Extraction Image Segmentation Knowledge Engineering Technique Neural Nets Neural Network-based Semantic Segmentation Model Object Recognition Optical, Image And Video Signal Processing Robot Perception Surrounding Objects Unsupervised Learning Video Signal Processing
Content Type	Text
Resource Type	Article

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