Camera and YOLO Experiments¶

YOLO camera detection is experimental and is not part of the production kiosk. The kiosk's optional --camera-capture path records high-speed video without running this object-detection experiment.

Use this guide only to evaluate a CSI camera such as the optional InnoMaker OV9281 global-shutter module. OpenFlight shot measurements still come from the radars.

Prerequisites¶

The experiment script requires these modules in the Pi's Python environment:

picamera2 for CSI camera capture;
ultralytics for YOLO inference; and
opencv-python for image processing and display.

They are intentionally absent from OpenFlight's normal install. Install them in a separate experimental environment appropriate for your Raspberry Pi OS image; do not add them to the production kiosk unless camera support is being restored.

Check the camera¶

Confirm Raspberry Pi OS sees the module before debugging OpenFlight:

rpicam-hello --list-cameras

Then run a short headless capture with an existing YOLO model:

uv run python scripts/vision/test_yolo_detection.py \
  --model models/golf_ball_yolo11n_new_256.onnx \
  --headless --num-frames 10

For a desktop preview on the Pi:

DISPLAY=:0 uv run python scripts/vision/test_yolo_detection.py \
  --model models/golf_ball_yolo11n_new_256.onnx \
  --imgsz 256 --threaded

Useful options¶

Option	Purpose	Default
`--imgsz`	YOLO inference size; smaller is faster	256
`--width` / `--height`	Camera capture resolution	640 × 480
`--fps`	Requested camera frame rate	60
`--confidence`	Minimum detection confidence	0.3
`--threaded`	Separate capture and inference threads	off
`--no-display`	Skip overlay display while benchmarking	off
`--buffer-count`	Camera buffers; fewer reduces latency	2
`--image PATH`	Test one saved image instead of the camera	unset

Start at --imgsz 256. Reduce it if inference is too slow, or increase it when the ball is too small to detect reliably. Measure performance on the actual Pi; frame rate depends on the model, runtime, resolution, and thermal state.

Model export¶

Export a PyTorch model to ONNX:

uv run python scripts/vision/test_yolo_detection.py \
  --model models/golf_ball_yolo11n.pt \
  --imgsz 256 --export-onnx

OpenVINO export is also supported with --export-openvino; add --int8 only after checking the accuracy loss on representative ball images.

Production status¶

The server does not integrate this YOLO tracker. Adding camera-assisted measurement would require dependency packaging, startup integration, hardware validation, and tests; this benchmark script alone does not enable it.