Summary of Cross-modal Semantic Segmentation For Indoor Environmental Perception Using Single-chip Millimeter-wave Radar Raw Data, by Hairuo Hu et al.
Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data
by Hairuo Hu, Haiyong Cong, Zhuyu Shao, Yubo Bi, Jinghao Liu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 A cross-modal semantic segmentation model based on a single-chip millimeter-wave (mmWave) radar is proposed for indoor environmental perception in firefighting and rescue operations. The model utilizes an automatic label generation method combining LiDAR point clouds, occupancy grid maps, and U-Net architecture with spatial attention to improve performance. Experimental results show that cross-modal semantic segmentation provides a more accurate representation of indoor environments, unaffected by azimuth, although performance declines with increasing distance. Using raw ADC data is ineffective, while RD tensors are more suitable for the proposed model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new technology helps firefighters and rescuers better understand their surroundings indoors using radar signals. The innovation uses a special type of radar called millimeter-wave (mmWave) that can see through walls. To make this work, scientists developed a way to automatically add labels to the data so the model can learn. They also created a special part called spatial attention to help the model focus on important areas. This new approach provides a more accurate picture of the environment and is unaffected by the direction from which it’s seen. While it’s not perfect at long distances, this technology could be very helpful in emergency situations. |
Keywords
» Artificial intelligence » Attention » Semantic segmentation
