# Yolo Detection 2026 Openvino

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

- Repository: https://github.com/SharpAI/DeepCamera (3124 stars)
- Category: DevOps
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-openvino -g -a claude-code -y`
Codex: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-openvino -g -a codex -y`
Cursor: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-openvino -g -a cursor -y`
Gemini CLI: `npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-openvino -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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)                      │
└─────────────────────────────────────────────────────┘
```

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

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

## Model

The rest: https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/detection/yolo-detection-2026-openvino/SKILL.md