yolo-detection-2026-openvino

OpenVINO — real-time object detection via Docker (NCS2, Intel GPU, CPU)

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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

┌─────────────────────────────────────────────────────┐
│ 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)                      │
└─────────────────────────────────────────────────────┘

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

Platform Setup

Linux

# 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+)

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

Windows

# 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:

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

Supported Devices

Device	Flag	Precision	~Speed
Intel NCS2	`MYRIAD`	FP16	~15ms
Intel iGPU	`GPU`	FP16/INT8	~8ms
Intel Arc	`GPU`	FP16/INT8	~4ms
Any CPU	`CPU`	FP32/INT8	~25ms
Auto	`AUTO`	Best	Auto

Protocol

Same JSONL as yolo-detection-2026:

Skill → Aegis (stdout)

{"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

./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.

Repository: SharpAI/DeepCamera
Path: skills/detection/yolo-detection-2026-openvino/SKILL.md
Commit: 2264fcb

Last updated: 1 day ago
First committed: 4 months ago

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