Open AccessNeural network‐based semantic segmentation model for robot perception of driverless vision
Author(s) -
Ye Lu,
Duan Ting,
Zhu Jiayi
Publication year - 2020
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/iet-csr.2020.0040
Subject(s) - segmentation , computer science , artificial intelligence , perception , computer vision , artificial neural network , robot , image segmentation , set (abstract data type) , enhanced data rates for gsm evolution , pattern recognition (psychology) , neuroscience , biology , programming language
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.