RTSeg: Real-Time Semantic Segmentation Comparative Study
Open accessAbstract
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 at11https://github.com/MSiam/TFSegmentation.
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Bojarski et al. demonstrated end-to-end learning for steering [2] and recently it was applied to fisheye cameras [54].
“Bojarski et al. demonstrated end-to-end learning [2] for steering and recently it was applied to fisheye cameras [54].”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [54]
The Abstract and Introduction discuss semantic segmentation, but neither addresses end-to-end steering with fisheye cameras. Searches for fisheye steering and Bojarski found no relevant passage in our copy. (LLM, conf=0.76)
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Learning Panoptic Segmentation from Instance Contours
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Semantic Segmentation of Land Cover in Multisource Aerial Imagery Using U-Net
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- Authors
- Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., & Jagersand, M.
- Affiliations
- Cairo University (EG)
- Venue
- 2018 25th IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1603-1607
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2790381683
- Cited by
- 112
- References
- 21
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 2 · 2023: 7 · 2022: 16 · 2021: 20 · 2020: 27 · 2019: 25 · 2018: 6
- FWCI
- 5.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- real-time semantic segmentation, benchmarking framework, computational efficiency, feature extraction, decoding algorithms, meta-architecture, cityscape, autonomous driving, robotics, modular network design
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