Summary of Hyperspectral Imaging-based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models, by Imad Ali Shah et al.
Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models
by Imad Ali Shah, Jiarong Li, Martin Glavin, Edward Jones, Enda Ward, Brian Deegan
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The abstract discusses the application of Hyperspectral Imaging (HSI) to Advanced Driving Assistance Systems (ADAS) perception. It highlights the advantages of HSI over traditional RGB imaging and notes the availability of datasets such as HyKo, HSI-Drive, HSI-Road, and Hyperspectral City. The paper presents a comprehensive evaluation of semantic segmentation models (SSMs) using these datasets, including baseline SSMs like DeepLab v3+, HRNet, PSPNet, and U-Net with variants. The results indicate that UNet-CBAM outperforms other SSMs by extracting channel-wise features and leveraging spectral information for enhanced semantic segmentation. This study establishes a benchmark for future evaluation of HSI-based ADAS perception. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HSI is used to improve driving safety. It’s like having superpower eyes on the road! The paper looks at how well different computer models do in understanding what they’re seeing, using special datasets and metrics. One model stands out as being really good at finding things on the road. This study helps us understand how HSI can make self-driving cars safer. |
Keywords
» Artificial intelligence » Semantic segmentation » Unet
