Summary of Automatic Image Annotation For Mapped Features Detection, by Maxime Noizet (utc et al.
Automatic Image Annotation for Mapped Features Detection
by Maxime Noizet, Philippe Xu, Philippe Bonnifait
First submitted to arxiv on: class=“authors”>Authors:<a href=“https://arxiv.org/search/cs?searchtype=author&query=Noizet,+M”
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 improving the accuracy of road feature detection in autonomous driving and localization applications. Specifically, it focuses on detecting poles, which are crucial for precise navigation. To achieve this, the authors introduce a multi-modal automatic annotation method that combines three different approaches: feature projection from a high-accuracy vector map combined with lidar data, image segmentation, and lidar segmentation. The resulting annotations are then used to fine-tune an object detection model for pole base detection using unlabeled data. Experimental results demonstrate significant improvements in pole detection accuracy compared to manual annotation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make self-driving cars better at recognizing roads and finding their way around. One important part of this is detecting poles, which are really common along roads. Right now, making computers recognize these poles takes a lot of human work, but the authors of this paper want to find a way to do it automatically. They tried combining three different methods to make the job easier and more accurate. This new approach worked really well and can help make self-driving cars even better at navigating roads. |
Keywords
» Artificial intelligence » Image segmentation » Multi modal » Object detection
