# RTSeg: Real-time Semantic Segmentation Comparative Study

arXiv: https://arxiv.org/abs/1803.02758 · HTML: https://mlsanity.com/paper/1803.02758/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 11 of 11)

- [HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers](https://mlsanity.com/paper/2106.06560/) (2021)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [WaveMix: A Resource-efficient Neural Network for Image Analysis](https://mlsanity.com/paper/2205.14375/) (2022)
- [WeatherProof: A Paired-Dataset Approach to Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2312.09534/) (2023)
- [On Efficient Real-Time Semantic Segmentation: A Survey](https://mlsanity.com/paper/2206.08605/) (2022)
- [Efficient Palm-Line Segmentation with U-Net Context Fusion Module](https://mlsanity.com/paper/2102.12127/) (2021)
- [WeatherProof: Leveraging Language Guidance for Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2403.14874/) (2024)
- [Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception](https://mlsanity.com/paper/2202.08490/) (2022)
- [Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation](https://mlsanity.com/paper/2406.05837/) (2024)
- ["ScatSpotter" -- A Dog Poop Detection Dataset](https://mlsanity.com/paper/2412.16473/) (2024)
- [PDE-Constrained Optimization for Neural Image Segmentation with Physics Priors](https://mlsanity.com/paper/2602.01069/) (2026)

## Abstract (arXiv)

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".