Yolo Detection 2026 Openvino

OpenVINO Object Detection

Real-time object detection using Intel OpenVINO runtime. Runs inside Docker for cross-platform support. Supports Intel NCS2 USB stick, Intel integrated GPU, Intel Arc discrete GPU, and any x86_64 CPU.

Requirements

• Docker Desktop 4.35+ (all platforms)
• Optional hardware: Intel NCS2 USB, Intel iGPU, Intel Arc GPU
• Falls back to CPU if no accelerator present

How It Works

Copy code
┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI)                                     │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ Docker Container              │       │
│              │   detect.py                   │       │
│              │   ├─ loads OpenVINO IR model   │       │
│              │   ├─ reads frame from volume   │       │
│              │   └─ runs inference on device  │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ /dev/bus/usb (NCS2)                       │
│   DRI ──→ /dev/dri (Intel GPU)                      │
└─────────────────────────────────────────────────────┘

1. Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ volume
2. Sends frame event via stdin JSONL to Docker container
3. detect.py reads frame, runs inference via OpenVINO
4. Returns detections event via stdout JSONL
5. Same protocol as yolo-detection-2026 — Aegis sees no difference

Platform Setup

Linux

bashCopy code
# Intel GPU and NCS2 auto-detected via /dev/dri and /dev/bus/usb
# Docker uses --device flags for direct device access
./deploy.sh

macOS (Docker Desktop 4.35+)

bashCopy code
# Docker Desktop USB/IP handles NCS2 passthrough
# CPU fallback always available
./deploy.sh

Windows

powershellCopy code
# Docker Desktop 4.35+ with USB/IP support
# Or WSL2 backend with usbipd-win for NCS2
.\deploy.bat

Model

Ships without a pre-compiled model by default. On first run, detect.py will auto-download yolo26n.pt and export to OpenVINO IR format. To pre-export:

bashCopy code
# Runs on any platform (unlike Edge TPU compilation)
python scripts/compile_model.py --model yolo26n --size 640 --precision FP16

Supported Devices

Protocol

Same JSONL as yolo-detection-2026:

Skill → Aegis (stdout)

jsonlCopy code
{"event": "ready", "model": "yolo26n_openvino", "device": "GPU", "format": "openvino_ir", "classes": 80}
{"event": "detections", "frame_id": 42, "camera_id": "front_door", "objects": [{"class": "person", "confidence": 0.85, "bbox": [100, 50, 300, 400]}]}
{"event": "perf_stats", "total_frames": 50, "timings_ms": {"inference": {"avg": 8.1, "p50": 7.9, "p95": 10.2}}}

Bounding Box Format

[x_min, y_min, x_max, y_max] — pixel coordinates (xyxy).

Installation

bashCopy code
./deploy.sh

The deployer builds the Docker image locally, probes for OpenVINO devices, and sets the runtime command. No packages pulled from external registries beyond Docker base images and pip dependencies.