Summary of Real-time Ship Recognition and Georeferencing For the Improvement Of Maritime Situational Awareness, by Borja Carrillo Perez
Real-time Ship Recognition and Georeferencing for the Improvement of Maritime Situational Awareness
by Borja Carrillo Perez
First submitted to arxiv on: 7 Oct 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 paper proposes an investigation into leveraging deep learning and computer vision to advance real-time ship recognition and georeferencing for improving maritime situational awareness. A novel dataset, ShipSG, is introduced, containing 3,505 images and 11,625 ship masks with corresponding class and geographic position. The authors design a custom real-time segmentation architecture, ScatYOLOv8+CBAM, which outperforms state-of-the-art methods by over 5%. To improve small and distant ship recognition in high-resolution images on embedded systems, an enhanced slicing mechanism is introduced. Additionally, a georeferencing method is proposed, achieving positioning errors of 18 m for ships up to 400 m away and 44 m for ships between 400 m and 1200 m. The approach is applied in real-world scenarios, such as detecting abnormal ship behavior, camera integrity assessment, and 3D reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses deep learning and computer vision to improve maritime situational awareness by recognizing and georeferencing ships in real-time. A new dataset and a custom architecture are designed to recognize ships and their locations. The approach outperforms existing methods and can be used for real-world applications like detecting unusual ship behavior or reconstructing 3D images. |
Keywords
» Artificial intelligence » Deep learning
