Summary of Improving the Detection Of Small Oriented Objects in Aerial Images, by Chandler Timm C. Doloriel and Rhandley D. Cajote
Improving the Detection of Small Oriented Objects in Aerial Images
by Chandler Timm C. Doloriel, Rhandley D. Cajote
First submitted to arxiv on: 12 Jan 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 paper presents an innovative approach to detecting small, oriented objects in large-scale aerial images. The current state-of-the-art methods focus mainly on orientation modeling, leaving room for improvement in terms of size detection. To address this gap, the authors propose a method that enhances classification and regression tasks in oriented object detection models. They design the Attention-Points Network, comprising two losses: Guided-Attention Loss (GALoss) and Box-Points Loss (BPLoss). GALoss utilizes an instance segmentation mask as ground-truth to learn attention features for small object detection, while BPLoss predicts box points relative to the target oriented bounding box. The network is tested on the DOTA-v1.5 and HRSC2016 datasets, showcasing its effectiveness in detecting small objects. The authors provide publicly available code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines better see tiny things in big aerial pictures! Right now, it’s tricky for computers to find these little objects because they’re so small and oriented (pointing) in different directions. Current methods are good at figuring out which way the objects point but not as good at finding them if they’re really small. To solve this problem, scientists created a new way to train computer models that can better detect these tiny objects. They call it the Attention-Points Network and it uses two special losses (ways of measuring how well the model is doing) to help it learn. This network was tested on some standard datasets and showed great results! Now, anyone can use this code to try out the new method. |
Keywords
» Artificial intelligence » Attention » Bounding box » Classification » Instance segmentation » Mask » Object detection » Regression
