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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
The pascal visual object classes (VOC) challenge
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ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network
Mehta, S., Rastegari, M., Shapiro, L., & Hajishirzi, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00941 · Cited by 551
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 334
“This has been explored in [49, 57, 48] for narrow angle cameras.”
BackgroundSelf-citationEfficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Lo, S.-Y., Hang, H., Chan, S.-W., & Lin, J.-J. (2019) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3338533.3366558 · Cited by 259
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationPreprint
Li, G., Yun, I., Kim, J., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11357 · Cited by 264
Polarimetric Convolutional Network for PolSAR Image Classification
Liu, X., Jiao, L., Tang, X., Sun, Q., & Zhang, D. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2879984 · Cited by 134
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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