Ghost-UNet: An Asymmetric Encoder-Decoder Architecture for Semantic Segmentation from Scratch

Iman Abaspur Kazerouni, Gerard Dooly, Daniel Toal

Research output: Contribution to journalArticlepeer-review

Abstract

One of the most important key points in the intelligent transportation systems is scene understanding of the known and unknown surrounding environment to achieve a safe driving for smart mobile robots and cars. Semantic segmentation can address most of the perception needs of mobile robots and Intelligent Vehicles (IV). There are several deep learning approaches based on Convolutional Neural Network (CNN) for semantic segmentation. Most of these techniques have been designed on a pretrained network base and loading a specific weight file is necessary for them. In this paper, we propose a deep architecture for semantic segmentation from scratch based on an asymmetry encoder- decoder architecture using Ghost-Net and U-Net which we have called it Ghost-UNet. This model can be used for precise segmentation using a combination of low-level spatial information and high-level feature maps. We focus our work on outdoor datasets to evaluate the proposed model which is tested on the Cityscapes dataset. The proposed model has good pixel accuracy and mean Intersection over Union (mIoU) compared with other valid literature.

Original languageEnglish
Article number9475058
Pages (from-to)97457-97465
Number of pages9
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • deep learning
  • Mobile robots
  • scene understanding
  • semantic segmentation

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