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    Drone Civilian / Armed Military Detection Dataset

    Drone Civilian / Armed Military Detection Dataset sample 1

    This dataset enables drone-mounted AI systems to distinguish between civilian and armed military personnel from aerial perspectives. Images are rendered from UAV altitudes of 20–100 meters across diverse terrains including desert, forest, urban, and rural environments. The dataset covers scenarios with mixed civilian-military presence, convoy movements, and checkpoint situations. Annotations in YOLO format include classes for civilian, armed_person, and vehicle. The full package contains 800 high-resolution images with evaluation videos for benchmarking real-time aerial detection. This is an enterprise-only dataset with no open-source sample due to the sensitive nature of the application.

    800 images

    Full Package

    YOLO

    Annotation Format

    100%

    Privacy Compliant

    Dataset Features

    Aerial UAV perspectives (20–100m altitude)
    Civilian vs armed personnel classification
    Desert, forest, urban, rural terrains
    Convoy and checkpoint scenarios
    YOLO format annotations
    Enterprise-only full package

    Intended Use Cases

    Military aerial reconnaissanceBorder security monitoringPeacekeeping operationsDefense AI development

    Free Sample vs. Commercial Package

    Free Open-Source Sample

    No open-source sample is published for this dataset. Full access is enterprise-only — Contact Us for details.

    Commercial Package

    The Creative Commons licence above applies only to the free sample, not to the full commercial package.

    Limitations & Recommended Validation

    This dataset is 100% synthetic. While it is designed to closely match real-world sensor and camera conditions, synthetic imagery can still differ from live footage in ways that affect model accuracy (a "domain gap"). Validate a trained model against real-world footage from your specific deployment environment before production use.

    Not intended as a sole basis for biometric identification, legal evidence, or safety-critical decisions without independent human review and real-world testing.