In the rapidly evolving maritime domain, ensuring the security and efficient management of port areas is paramount. This research introduces a comprehensive drone-based surveillance system tailored for monitoring vessels in port areas and detecting encroachments. The proposed framework comprises two distinct drone systems: a primary hexacopter system and a secondary quadcopter system. The primary system, equipped with a high-quality camera and JETSON nano for realtime frame processing using Convolution Neural Network (CNN), offers extensive area coverage. In contrast, the secondary system addresses the angle limitations of the primary system, focusing on specific regions and tracking suspicious waterborne vehicles. Both systems utilize advanced sensors and technologies, including LiDAR for obstacle avoidance and YOLO v8 CNN for object detection. Through real-time streaming protocol (RTSP), the processed frames are transmitted to control centers, enabling timely decision-making. The system's efficacy is demonstrated through various use cases, including the detection of unauthorized waterborne vehicles and harbor crafts, tracking specific vessels, and monitoring docking statuses.


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    Titel :

    Enhancing Port Surveillance: A Dual-Drone System for monitoring vessels and detecting Encroachments


    Beteiligte:
    Jani, Devesh (Autor:in) / Shah, Sakshi (Autor:in) / Mahindroo, Shashwat (Autor:in) / Patel, Dharmil (Autor:in) / Upadhyaya, Trushit (Autor:in)


    Erscheinungsdatum :

    14.12.2023


    Format / Umfang :

    769540 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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