icey-zhang/SuperYOLO
A YOLO-based object detection model that uses super-resolution assistance for detecting objects in multimodal (RGB+IR) satellite and aerial imagery.
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SuperYOLO is a deep learning object detection system designed for remote sensing imagery. It combines YOLO-based object detection with a super-resolution branch to improve detection accuracy by processing high-resolution (1024x1024) images downsampled to 512x512. The model performs multimodal fusion using RGB and infrared channels and transforms labels for horizontal bounding box detection. It was published in IEEE Transactions on Geoscience and Remote Sensing.
Frequently asked
- What is icey-zhang/SuperYOLO?
- A YOLO-based object detection model that uses super-resolution assistance for detecting objects in multimodal (RGB+IR) satellite and aerial imagery.
- Is SuperYOLO open source?
- Yes — icey-zhang/SuperYOLO is an open-source project tracked on heatdrop.
- What language is SuperYOLO written in?
- icey-zhang/SuperYOLO is primarily written in Python.
- How popular is SuperYOLO?
- icey-zhang/SuperYOLO has 486 stars on GitHub.
- Where can I find SuperYOLO?
- icey-zhang/SuperYOLO is on GitHub at https://github.com/icey-zhang/SuperYOLO.