Neural network‐based semantic segmentation model for robot perception of driverless vision. Issue 4 (10th November 2020)

• Record Type: Journal Article
• Authors: Ye, Lu; Duan, Ting; Zhu, Jiayi
• Is Part Of: IET cyber-systems and robotics. Volume 2:Issue 4(2020)
• Page Start: 190
• Page End: 196
• Publication Date: 2020-11-10
• Subjects: video signal processing -- unsupervised learning -- feature extraction -- image segmentation -- neural nets -- object recognition; neural network‐based semantic segmentation model -- robot perception -- driverless vision -- driverless vehicles -- surrounding objects; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/iet-csr.2020.0040
• Languages: English
• ISSNs: 2631-6315

Abstract

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.