Summary of Improving Apple Object Detection with Occlusion-enhanced Distillation, by Liang Geng
Improving Apple Object Detection with Occlusion-Enhanced Distillation
by Liang Geng
First submitted to arxiv on: 3 Sep 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 In the field of object detection, visual obstructions from leaves and branches can lead to increased false detections. To address this issue, we introduce Occlusion-Enhanced Distillation (OED), a technique that utilizes occlusion information to regularize feature learning on occluded datasets. OED combines Grounding DINO and SAM methods to create occlusion examples reflecting natural fruit growth states. A multi-scale knowledge distillation strategy guides the student network to learn from the teacher across scales, enhancing robustness. EMA (Exponential Moving Average) aids in learning generalized features. Our approach outperforms state-of-the-art techniques through comparative experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find apples on a tree with lots of leaves and branches getting in the way. It’s hard! Scientists are working on making it easier by creating better tools for detecting objects, even when they’re hidden. They came up with a new method called Occlusion-Enhanced Distillation (OED). OED helps the tool learn to recognize objects by using information about what’s hiding them. The goal is to make the tool more accurate and reliable. This new approach did better than other methods in tests. |
Keywords
» Artificial intelligence » Distillation » Grounding » Knowledge distillation » Object detection » Sam
