RTSeg: Real-Time Semantic Segmentation Comparative Study
By Mennatullah Siam, M. Gamal, Moemen Abdel-Razek, Senthil Kumar Yogamani, Martin Jägersand. 2018
121 citations recorded by the source corpus.
Semantic segmentation benefits robotics related applications, especially autonomous driving. Most of the research on semantic segmentation only focuses 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 11https://github.com/MSiam/TFSegmentation.
Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Senthil Kumar Yogamani
- Ganesh Sistu
- Stefan Milz
- Sumanth Chennupati
- Karl Amende
- Michal Uřičář
- Martin Simon
- Hazem Rashed
In Evaluation & Benchmarks
- Ciarán Hughes
- Patrick Pérez
- Karl Amende
- Stefan Milz
- Christian Witt
- Martin Simon
- Senthil Kumar Yogamani
- Sumanth Chennupati
Citations in the corpus
- WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “This has been explored in [49, 57, 48] for narrow angle cameras.”
- RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving (2019, methodology): “A detailed survey for image semantic segmentation for autonomous driving is presented in [19] and efficient design techniques were discussed in [2] [20].”
- All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, methodology): “Theses methods explore the image crop/resizing [367] or efficient network design [368], [369] or transfer learning [370], [371].”