Summary of Exploiting Low-level Representations For Ultra-fast Road Segmentation, by Huan Zhou et al.
Exploiting Low-level Representations for Ultra-Fast Road Segmentation
by Huan Zhou, Feng Xue, Yucong Li, Shi Gong, Yiqun Li, Yu Zhou
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to road segmentation on embedded platforms, which is essential for achieving real-time and high accuracy. The authors argue that roads can be represented using low-level features instead of the traditional high-level features, leading them to develop the Low-level Feature Dominated Road Segmentation network (LFD-RoadSeg). This architecture consists of a bilateral structure with two branches: spatial detail and context semantic. The spatial detail branch extracts low-level feature representation from the first stage of ResNet-18, while the context semantic branch extracts context features in a fast manner using an asymmetric downsampling approach and an aggregation module. A selective fusion module is then used to calculate pixel-wise attention between the low-level representation and context feature, suppressing non-road responses. The authors evaluate LFD-RoadSeg on KITTI-Road, achieving a maximum F1-measure of 95.21% and average precision of 93.71%, while maintaining a compact model size of just 936k parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to better understand roads using computer vision. Usually, we try to recognize specific objects like cars or buildings, but this time the researchers are trying to focus on the road itself as just a background. They found that they can use simple features from an image to figure out what’s road and what’s not. To do this, they created a special kind of network called LFD-RoadSeg. It has two parts: one that looks at small details in the picture and another that looks at bigger context. Then it combines these two ideas to make decisions about what’s road and what’s not. The researchers tested their idea on some images and found that it worked really well, even on devices like phones or embedded systems. |
Keywords
* Artificial intelligence * Attention * Precision * Resnet
