AI Model — YOLO11s#

The UAV Vision Analytics application uses YOLO11s, Ultralytics’ stock COCO-pretrained object detection model.

Model Details#

Property	Value
Model	YOLO11s
Source	Ultralytics (downloaded by the `yolo` CLI)
Precision	FP16 (OpenVINO IR)
Input resolution	640 × 640
Detection classes	80 classes (person, car, truck, bus, bicycle, motorcycle, …)
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 Ultralytics’ release assets 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 yolo11s.pt from Ultralytics, 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/
    └── yolo11s/
        ├── yolo11s.pt                     ← downloaded PyTorch checkpoint
        └── yolo11s_openvino_model/
            ├── yolo11s.xml                ← OpenVINO IR model definition
            └── yolo11s.bin                ← model weights

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

/home/pipeline-server/resources/models/yolo11s/yolo11s_openvino_model/yolo11s.xml

Using a Different Ultralytics Model#

To try a different size/variant (e.g. yolo11n, yolo11m, or yolov8n), edit the model= value in the model target of the Makefile (it accepts any Ultralytics model name and auto-downloads the matching checkpoint), then update the model path in every file that references it:

Makefile (MODEL_DIR, MODEL_XML)
scripts/mavlink_pipeline_manager.py (MODEL_PATH)
scripts/uavsdk_pipeline_manager.py (MODEL_PATH)
benchmark/benchmark_app_payload.json (all model fields)

Using a Custom / Fine-Tuned Model#

To substitute a custom-trained OpenVINO IR model (e.g. a model fine-tuned on aerial imagery):

Place model.xml + model.bin under resources/models/{{MODEL_NAME}}/
Update the model path in the files listed above
Re-verify threshold (default 0.4) is appropriate for the new model’s confidence distribution

Note: Only OpenVINO IR format (.xml + .bin) is supported by gvadetect. ONNX models must be converted first with mo (OpenVINO Model Optimizer) or openvino.convert_model().