Summary of Dassf: Dynamic-attention Scale-sequence Fusion For Aerial Object Detection, by Haodong Li et al.
DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection
by Haodong Li, Haicheng Qu
First submitted to arxiv on: 18 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 detection of small objects in aerial images is a crucial task in computer vision. The original YOLO algorithm struggles to detect targets of different scales due to its limited ability to perceive these variations. To address this issue, this paper proposes the dynamic-attention scale-sequence fusion algorithm (DASSF) for small target detection. The DASSF algorithm consists of three key components: a dynamic scale sequence feature fusion module that enhances up-sampling and reduces computational load; an x-small object detection head that boosts detection capability of small targets; and a dynamic head that improves the model’s expressive ability to detect targets of different types and sizes. Experimental results demonstrate that DASSF outperforms mainstream methods, achieving an increase in mean average precision (mAP) on the VisDrone-2019 and DIOR datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer vision: detecting small objects in aerial images. The original method, called YOLO, doesn’t do very well at this task because it can’t handle objects of different sizes. To fix this, the researchers created a new algorithm that combines three ideas to make object detection better. They tested their new algorithm on some really hard datasets and found that it works much better than the old method, increasing the accuracy by 9.2% and 2.4%. This is important because aerial images are used in many fields like mapping, surveillance, and more. |
Keywords
» Artificial intelligence » Attention » Mean average precision » Object detection » Yolo
