Please use this identifier to cite or link to this item:
Title: PVT3: a pruned video-vision transformer for tactile texture classification
Authors: Ouyang, Yanjia
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Ouyang, Y. (2022). PVT3: a pruned video-vision transformer for tactile texture classification. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: B3127-211
Abstract: With the newly involved technologies in tactile sensory, variants tactile sensors have been deployed on robots which provides them touching ability to perceive complex environments. One typical example of robot touching task is to recognize different materials based on the tactile data generated from different textures. In this report, we propose PVT 3 , a light-weight Transformer-based architecture with pruning layers to model the texture representation. By using a Video-Vision Transformer backbone, the spatial and temporal features will be well preserved and utilized. The multi-dimensional pruning layers will reduce model complexity and size without sacrificing the performance. Three tactile datasets are used for 3 testing the PVT model. Overall, our proposed model achieves higher accuracy on material classification results with a smaller model size compared to the state-of-the-art tactile texture models. This work was written as a paper and submitted to the International Conference on Intelligent Robots and Systems (IROS) 2022.
Schools: School of Electrical and Electronic Engineering 
Organisations: A*STAR -I2R
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
6.09 MBAdobe PDFView/Open

Page view(s)

Updated on Feb 27, 2024

Download(s) 50

Updated on Feb 27, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.