Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

Applied Sciences

Angelos Amanatiadis¹  Lazaros Tsochatzidis²  Ilias Papadeas²  Ioannis Pratikakis² 

[1] Department of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece;Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece;

关键词: semantic image segmentation;    real time;    deep learning;    autonomous driving;   

DOI  :  10.3390/app11198802

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown