RTSeg: Real-time Semantic Segmentation Comparative Study
Abstract
Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework is publicly available at "https://github.com/MSiam/TFSegmentation".
Cite
@article{arxiv.1803.02758,
title = {RTSeg: Real-time Semantic Segmentation Comparative Study},
author = {Mennatullah Siam and Mostafa Gamal and Moemen Abdel-Razek and Senthil Yogamani and Martin Jagersand},
journal= {arXiv preprint arXiv:1803.02758},
year = {2020}
}
Comments
Accepted in IEEE ICIP 2018. IEEE Copyrights: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses