weecology / weecology/DeepForest
Point to box detector.
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 774
- Forks
- 265
- PR merge metrics
- No merged PRs in 30d
Description
Users often have points at tree stems, or annotations at class centers. The current strategy is generate tree crown proposals using an object detection network, such as the retinanet baselines and then heuristically match the points to the boxes. This isn't particularly satisfying, as we quickly get into all sorts of edge cases with multiple points per box, multiple boxes per point. It would be better to learn a transformation for a specific task.
https://arxiv.org/abs/2207.06827
We could easily create a benchmark dataset of points and then verified boxes based on some auxiliary statistics or field crowns.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the current tree-crown proposal and heuristic point-to-box matching approach, including the RetinaNet baselines and the linked paper. Define the benchmark dataset and the task-specific transformation, then establish evaluation criteria showing that points are reliably converted into verified boxes across multiple-point and multiple-box cases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100