TRACK MY VOYAGE is an intelligent surveillance solution that leverages deep learning and computer vision to detect, classify, and monitor maritime vessel activity using satellite and aerial imagery. This system aims to enhance maritime border security by automatically identifying unauthorized or suspicious ship movements, reducing reliance on manual monitoring.

Monitoring maritime regions to detect unauthorized or suspicious ship activity is a critical challenge for coastal and national security. Traditional surveillance methods often rely on manual analysis of satellite images, which is time-consuming and prone to human error. With increasing maritime traffic and security threats, there is a growing need for an automated, intelligent system capable of detecting and classifying ships in satellite imagery. A smart surveillance system should be able to detect ships, identify their types, and generate alerts when vessels cross predefined maritime boundaries. This will enhance efficiency, accuracy, and responsiveness in maritime border security operations.

• To understand the limitations of traditional ship monitoring systems and the potential of satellite image-based surveillance.
• To identify the technical requirements for automating ship detection, classification, and boundary alert generation using computer vision.
• To develop an efficient image-processing pipeline using deep learning models (e.g., YOLOv8) to detect and mark ships in satellite images or video frames.
• To classify detected ships based on type or size to support advanced surveillance use cases.
• To implement a geofencing mechanism that triggers alerts when a vessel crosses predefined boundaries.
• To design a modular system architecture that supports scalability, accuracy, and integration with real-time or batch-based maritime monitoring solutions.
• To ensure the solution is adaptable for further development, including person count estimation and integration with GIS tools or maritime databases.