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The Rolls-Royce led Advanced Autonomous Waterborne Applications Initiative (AAWA) project unveiled a vision of how remote and autonomous shipping will become a reality, changing the nature of the shipping industry.

The Rolls-Royce led Advanced Autonomous Waterborne Applications Initiative (AAWA) project unveiled a vision of how remote and autonomous shipping will become a reality, changing the nature of the shipping industry.

China unveils vision for 'Polar Silk Road' across Arctic.

3D animated videos of VTT and ROLSS-ROYCE's vision of seafaring in 2025.

Ground-breaking marine perception sensor provides high-definition situational awareness to eliminate at-sea collisions and allisions, and increase operational performance​​​​.

Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.