Ocean Eye: Advanced Neural Segmentation for High-Resolution Ship Detection

Abstract - Maritime safety and monitoring have become more important with the increase in global trade and ship traffic. This paper introduces Ocean Eye, a smart maritime surveillance system that can detect ships, track their movement, measure real-world distances, and identify possible collision risks. The system uses YOLOv8 for detecting objects, DeepSORT for tracking ships, and socket communication for handling real-time distress signals. It also supports restricted zone monitoring and provides a user-friendly dashboard for visualization. The proposed system improves maritime safety by delivering real-time alerts and helping in better decision-making, similar to modern monitoring systems. Plain Language Summary: This research introduces Ocean Eye, a smart system that helps monitor ships and improve safety in oceans. The system uses artificial intelligence to detect ships and track their movement in real time. It can calculate distances between ships and identify possible collision risks. It also monitors restricted areas and sends alerts when rules are violated or danger is detected. By providing real-time insights and warnings, the system helps improve maritime safety and supports better decision-making. Keywords – Maritime Surveillance, YOLOv8, DeepSORT, Collision Detection, Computer Vision, Ship Tracking, Socket Networking.