AI Model — YOLOv8n-VisDrone

The UAV Vision Analytics application uses YOLOv8n-VisDrone, an object detection model fine-tuned on the VisDrone dataset for detecting objects commonly seen in drone-view imagery.

Model Details

Property	Value
Model	YOLOv8n-VisDrone
Source	mshamrai/yolov8n-visdrone
Precision	FP16 (OpenVINO IR)
Input resolution	640 × 640
Detection classes	pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor
Ultralytics version	8.4.67 (pinned — see resources/requirements.txt)

Important: ultralytics is pinned to 8.4.67. Newer releases (8.4.115+ tested) changed the detection head’s box-decoding math to use a CumSum op instead of Range. The resulting OpenVINO IR runs fine on CPU but fails to compile on GPU and NPU plugins. Version 8.4.67 produces a Range-based graph verified on all three devices. Do not upgrade ultralytics without re-verifying GPU/NPU compatibility.

Prerequisites

• Python 3.10 or later with python3-venv support

• Internet access to reach Hugging Face and PyPI (configure proxy if behind a corporate firewall)

Install python3-venv (if missing)

make model creates a virtual environment via python3 -m venv. On Ubuntu 24 the venv support package must be installed separately:

sudo apt install python3.12-venv

Quick Setup — make model (recommended)

From the app root directory:

cd edge-ai-suites/federal-and-aerospace-ai-suite/uav-vision-analytics

make model

This creates resources/venv/, installs all dependencies, downloads best.pt from Hugging Face, and exports to OpenVINO FP16 IR.

Behind a proxy? Set proxy variables before running:

export https_proxy=http://proxy-org.com:port-number
export http_proxy=http://proxy-org.com:port-number

make model

Expected Output Path

After export, the model files are at:

resources/
└── models/
    └── yolov8n-visdrone/
        ├── best.pt                      ← downloaded PyTorch checkpoint
        └── best_openvino_model/
            ├── best.xml                 ← OpenVINO IR model definition
            └── best.bin                 ← model weights

The inference pipelines reference the model at the container-internal path:

/home/pipeline-server/resources/models/yolov8n-visdrone/best_openvino_model/best.xml