Optimizing Computational Load and Energy Efficiency in UAV-Based Port Surveillance System

The unauthorized entry of vessels into restricted port areas poses significant security risks and regulatory challenges. Traditional surveillance methods often fall short of providing timely and comprehensive monitoring, leading to potential security breaches and operational inefficiencies. We propose a system that utilizes image stitching technology onboard the drones for enhanced mapping and object detection applications. This research addresses the issue of identifying unauthorized watercraft and vessels entering authorized port regions and notifying the appropriate authorities by transmitting real-time coordinates of the intruding vessel. Unauthorized vessel detection is carried out by using a Deep Learning Algorithm on the Micro-processor onboard each drone. Pub-Sub Model is used for fast and secure communication between the Drones and the MIS. Authorities use drones to scan the designated area simultaneously, processing images by image stitching and storing them in the drones. It will process on the drone's platform and verify the authorization of the vessel, if the unauthorized vessel is detected, then The Image along with the coordinates of the vessel is sent to the MIS via the Pub-Sub Model. Drones process this by balancing the workload over a certain period.