The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
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 [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]
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Cites
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A Distributed Scheme for Accelerating Semantic Video Segmentation on An Embedded Cluster
Yang, H.-K., Fu, T.-J., Chiang, P.-H., Ho, K.-W., & Lee, C. (2019) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd46524.2019.00018 · Cited by 1
Barbosa, F. M., & Osório, F. S. (2021) · Anais do Computer on the Beach · doi:10.14210/cotb.v12.p072-079
Semantic Segmentation for People Detection on Beach Images
Monte, L. D. A., Oliveira, E. G., Cordeiro, F. R., & Macario, V. (2021) · doi:10.5753/eniac.2021.18295 · Cited by 1
Review of recent techniques used for Aerial Image Segmentation
Saksham, P., Samanta, A., & Minu, R. I. (2022) · International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst54752.2022.9781826
Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty
Shen, T.-C., Yang, C., Yen, I. E.-H., & Lin, S.-D. (2022) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa54385.2022.10032353
Hua, S., Cheng, J., Han, W., Xu, W., & Sheng, V. S. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-7161-9_18
Bilateral Dual-Residual Real-Time Semantic Segmentation Network
Xiang, S., Zhou, D., Tian, D., & Wang, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.060244
Lightweight Architectures for Binary Segmentation of Fresh Mud Deposits in UAV Imagery
Guastella, M., D’Alessandro, A., Pisciotta, A., & Martorana, R. (2025) · International Conference on Electromagnetics in Advanced Applications (ICEAA) · doi:10.1109/iceaa65662.2025.11305798
Krefft, L., & Ludwig Hoegner, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-12840-9_37
Scaling EfficientDet for Semantic Segmentation in Autonomous Delivery Robots
Lim, S., Lee, H., Kim, T., & Kim, H. S. (2026) · International Conference on Robotics and Automation Sciences (ICRAS) · doi:10.1109/icras70064.2026.11717994
Metadata
- 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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