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Select and Implement Object Detection & Tracking
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Linked challenge: Build an AI-Driven Multi-Drone Threat Detection & Prioritization System
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Linked challenge
Build an AI-Driven Multi-Drone Threat Detection & Prioritization System
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Choose a suitable real-time object detection model (e.g., YOLOv8) and a multi-object tracking algorithm (e.g., ByteTrack). Set up a development environment, integrate your chosen models, and implement the initial pipeline for detecting and tracking multiple drones in a provided simulated video stream. Ensure that each detected drone is assigned a persistent unique ID across frames. Use WizardLM-2 with CAMEL to generate a simple drone swarm scenario (e.g., 3 drones approaching from different angles) as initial test data.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
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Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.