We propose deploying a fleet of five autonomous drones for surveillance at ports. These drones stream live video to a central server equipped with three transfer learning based machine learning models. The first model tracks and categorizes vessels, cross-referencing their Hull Identification Numbers (HIN) with the database. Identified vessels are marked in green; unknown ones trigger red alerts and are mapped on a website. Violators are blacklisted upon confirmation. The second model identifies land encroachments by comparing drone-captured images with reference ones, detecting deviations using the Structural Similarity Index Measure (SSIM). The third model detects vessel encroachments by monitoring HINs and alerting authorities if a vessel remains stationary for too long. Alerts for both land and vessel encroachments are sent to port authorities via a dedicated website and a mobile app. A real-time dashboard provides harbor vessel counts, encroachment alerts, recent violations, and weather updates for drone operations, offering comprehensive oversight of port activities. This solution integrates autonomous drones, machine learning, and a user-friendly dashboard to enhance port management.


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    Title :

    Drone Surveillance Integrated with Machine Learning for Enhanced Port Management


    Contributors:
    Gomathy, G (author) / Prabakaran, Gs (author) / Livisha, K (author) / Parasuram, S (author) / Akash Raj, R (author)


    Publication date :

    2024-04-17


    Size :

    713425 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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