Enhanced Maritime Situational Awareness with Neuromorphic Cameras

Supervisors:

Primary supervisor Dr Saeed Afshar

Description:

The dynamic, complex, and often unpredictable marine environment necessitates sophisticated monitoring solutions for reliable situational awareness. Conventional camera systems, however, face limitations due to power consumption, data overload, and adaptability to rapid changes in illumination and motion. This project proposes the deployment of neuromorphic cameras, which mimic the operation of the human eye, to overcome these challenges.

Neuromorphic cameras, with their event-based operation, are highly efficient in power consumption, making them ideal for long-duration maritime surveillance operations. These cameras only capture and process changes in the scene, significantly reducing data volumes compared to traditional cameras.

The inherent adaptability of neuromorphic cameras to varying light conditions and their ability to capture high-speed motion make them especially useful in the maritime context. By accurately capturing and analyzing maritime activities in real-time, they can aid in improving navigation safety, identifying potential threats, and enhancing the overall situational awareness at sea.

Outcomes:

The project aims to harness the potential of neuromorphic cameras to enhance maritime situational awareness. By developing an efficient, responsive, and low-power neuromorphic sensor processor we aim to develop a real-time maritime surveillance, navigation, and overall safety at sea. This will include the following tasks:

• Literature Review: Conduct a comprehensive study on the current state of maritime situational awareness and the potential application of neuromorphic cameras in this domain.
• System Design: Design an effective maritime monitoring system incorporating neuromorphic cameras and suitable processing algorithms.
• System Implementation: Deploy the designed system using appropriate hardware and software tools.
• System Testing and Validation: Test the system in real maritime environments and validate its effectiveness, efficiency, and adaptability. Optimize based on performance results.
• Evaluation: Evaluate the overall impact of the system in terms of improving maritime situational awareness, including aspects of navigation safety, threat identification, and data handling efficiency.
• Communication and Publication: Document the results for publication in a relevant scientific journal. Present findings at industry conferences and seminars.

Eligibility criteria:

Experience in Python or C++ for designing and testing algorithms is crucial. Knowledge of neuromorphic hardware and software, image processing, and a sound understanding of maritime operations are beneficial.