Summary of Research on Edge Detection Of Lidar Images Based on Artificial Intelligence Technology, by Haowei Yang et al.
Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology
by Haowei Yang, Liyang Wang, Jingyu Zhang, Yu Cheng, Ao Xiang
First submitted to arxiv on: 14 Jun 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 paper proposes an edge detection method for LiDAR images using artificial intelligence technology to address the challenges faced by traditional methods in terms of accuracy and computational complexity. The study reviews current research on LiDAR technology and image edge detection, introducing common algorithms and their applications. A deep learning-based model is designed and implemented, optimized through preprocessing and enhancement of the LiDAR image dataset. Experimental results show that the proposed method outperforms traditional methods in terms of detection accuracy and computational efficiency, with significant practical application value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better detect edges in LiDAR images using AI technology. This is important because LiDAR tech is used for things like self-driving cars and robots. Traditional edge detection methods don’t work well for LiDAR images, so the researchers created a new method that uses deep learning. They tested it and showed that it’s more accurate and efficient than other methods. This can be really useful in real-life applications. |
Keywords
» Artificial intelligence » Deep learning
