Real-Time UAV Detection Pipeline
Fine-tuned YOLOv8 computer vision system capable of identifying and tracking multiple drone types in complex aerial environments with 95%+ mAP.
Autonomous Surveillance
The proliferation of hobbyist drones has created a need for specialized surveillance systems capable of distinguishing between legitimate aircraft, birds, and potential threats.
Multi-Class
Detects Quadcopters, Fixed-wing UAVs, and Birds simultaneously.
Low Latency
Optimized TensorRT engine for 30FPS+ processing on edge hardware.
Resilient
Robust performance in low light, rain, and cluttered urban backgrounds.
Technical Strategy
The core challenge was detecting small objects moving at high speeds in a massive 4K input stream. We solved this through a tiered detection strategy:
Custom Dataset Curation
Aggregated over 50,000 labeled images of UAVs in diverse orientations, distances, and lighting conditions to minimize false negatives.
YOLOv8 Fine-Tuning
Fine-tuned the YOLOv8-Medium model with a custom anchor-free head, optimized for the scale of drones at distances up to 300 meters.
TensorRT Optimization
Converted the PyTorch model to a FP16 TensorRT engine, achieving 2.5x speedup on edge inference hardware.
Leverage Deep Learning
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