Summary of Pointobb-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection, by Botao Ren et al.
PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection
by Botao Ren, Xue Yang, Yi Yu, Junwei Luo, Zhidong Deng
First submitted to arxiv on: 10 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 This research paper proposes a new method for single point supervised oriented object detection called PointOBB-v2. Unlike other approaches that rely on one-shot samples or powerful pre-trained models, PointOBB-v2 uses a prior-free feature to generate pseudo-rotated boxes from points. The approach involves generating a Class Probability Map (CPM) by training the network with non-uniform positive and negative sampling, which learns approximate object regions and contours. Principal Component Analysis (PCA) is then applied to estimate the orientation and boundary of objects. A separation mechanism is also incorporated to resolve confusion caused by overlapping on the CPM, enabling operation in high-density scenarios. The method achieves a training speed 15.58x faster and an accuracy improvement of 11.60%/25.15%/21.19% on the DOTA-v1.0/v1.5/v2.0 datasets compared to the previous state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to detect objects from a single point, without needing lots of data or powerful computers. The new method is faster and better than the old way, which is good news for people who need to do this kind of detection. It uses a special map to figure out where objects are and what they look like, and then it uses math to make sure everything is accurate and correct. |
Keywords
» Artificial intelligence » Object detection » One shot » Pca » Principal component analysis » Probability » Supervised
