Research Area:  Machine Learning

Abstract:

Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to operate in real time so that can efficiently support an autonomous driving scenario. To this end, the presented overview puts a particular emphasis on the presentation of all those approaches which permit inference time reduction, while an analysis of the existing methods is addressed by taking into account their end-to-end functionality, as well as a comparative study that relies upon a consistent evaluation framework. Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep learning for autonomous driving.

Keywords:  

Author(s) Name:  Ilias Papadeas,Lazaros Tsochatzidis ,Angelos Amanatiadis and Ioannis Pratikakis

Publisher name:  MDPI

DOI:  10.3390/app11198802

Volume Information:  Volume 11, Issue 19, 10.3390/app11198802

Paper Link:   https://www.mdpi.com/2076-3417/11/19/8802