Ship Segmentation and Georeferencing from Static Oblique View Images
Borja Carrillo-Perez1, Sarah Barnes1, Maurice Stephan1
1German Aerospace Center (DLR), Institute for the Protection of Maritime Infrastructures, Fischkai 1, 27572 Bremerhaven, Germany.
Sensors (Basel, Switzerland)
|April 12, 2022
Summary
This study introduces the ShipSG dataset for automated ship recognition and georeferencing in maritime surveillance. It evaluates segmentation methods, achieving high accuracy for real-time vessel monitoring and situational awareness.
Area of Science:
- Computer Vision
- Maritime Surveillance
- Geospatial Analysis
Background:
- Effective maritime situational awareness relies on accurate ship monitoring at ports.
- Current systems often lack the automation needed for real-time analysis and visualization of vessel data.
- Automated recognition and georeferencing of ships are crucial for enhancing maritime security and operational efficiency.
Purpose of the Study:
- To introduce the novel ShipSG dataset for ship segmentation and georeferencing in static oblique maritime monitoring scenes.
- To evaluate the performance of four instance segmentation methods (Mask-RCNN, DetectoRS, YOLACT, Centermask-Lite) for ship recognition.
- To propose and validate a novel method for georeferencing segmented ship masks using homography transformation.
Main Methods:
- Development of the ShipSG dataset featuring segmented and georeferenced ships.
- Comparative analysis of Mask-RCNN, DetectoRS, YOLACT, and Centermask-Lite for ship instance segmentation.
- Implementation of a homography-based method for transforming segmented ship pixels into geographic coordinates.
Main Results:
- DetectoRS achieved the highest mean Average Precision (mAP) of 0.747 for ship segmentation.
- Centermask-Lite demonstrated the fastest performance at 40.96 Frames Per Second (FPS).
- The proposed georeferencing method achieved accuracies of (22 ± 10) m within 400 m and (53 ± 24) m beyond 400 m.
Conclusions:
- The ShipSG dataset and proposed georeferencing method significantly advance automated maritime surveillance capabilities.
- Robust and real-time ship segmentation models are essential for effective maritime situational awareness.
- Accurate georeferencing of detected vessels enhances the utility of camera systems for port security and traffic management.
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