Agent Skills › SharpAI/DeepCamera › yolo-detection-2026-coral-tpu-win-wsl

yolo-detection-2026-coral-tpu-win-wsl

GitHub

基于Google Coral Edge TPU在Windows WSL环境下实现实时目标检测，支持80类COCO物体识别，通过USB桥接实现低延迟推理。

skills/detection/yolo-detection-2026-coral-tpu-win-wsl/SKILL.md SharpAI/DeepCamera

Trigger Scenarios

需要在Windows主机上利用Coral TPU进行边缘计算和实时视频分析需要配置WSL与硬件USB设备的连接以运行AI模型

Install

npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-win-wsl -g -y

More Options

Non-standard path

npx skills add https://github.com/SharpAI/DeepCamera/tree/master/skills/detection/yolo-detection-2026-coral-tpu-win-wsl -g -y

Use without installing

npx skills use SharpAI/DeepCamera@yolo-detection-2026-coral-tpu-win-wsl

指定 Agent (Claude Code)

npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-win-wsl -a claude-code -g -y

安装 repo 全部 skill

npx skills add SharpAI/DeepCamera --all -g -y

预览 repo 内 skill

npx skills add SharpAI/DeepCamera --list

SKILL.md

Frontmatter

{
    "icon": "assets\/icon.png",
    "name": "yolo-detection-2026-coral-tpu-win-wsl",
    "entry": "scripts\/wsl_wrapper.cjs",
    "mutex": "detection",
    "deploy": {
        "windows": "deploy.bat"
    },
    "runtime": "wsl-python",
    "version": "1.0.0",
    "category": "detection",
    "parameters": [
        {
            "name": "auto_start",
            "type": "boolean",
            "group": "Lifecycle",
            "label": "Auto Start",
            "default": false,
            "description": "Start this skill automatically when Aegis launches"
        },
        {
            "max": 1,
            "min": 0.1,
            "name": "confidence",
            "type": "number",
            "group": "Model",
            "label": "Confidence Threshold",
            "default": 0.5,
            "description": "Minimum detection confidence — lower than GPU models due to INT8 quantization"
        },
        {
            "name": "classes",
            "type": "string",
            "group": "Model",
            "label": "Detect Classes",
            "default": "person,car,dog,cat",
            "description": "Comma-separated COCO class names (80 classes available)"
        },
        {
            "name": "fps",
            "type": "select",
            "group": "Performance",
            "label": "Processing FPS",
            "default": 5,
            "options": [
                0.2,
                0.5,
                1,
                3,
                5,
                15
            ],
            "description": "Frames per second — Edge TPU handles 15+ FPS easily"
        },
        {
            "name": "input_size",
            "type": "select",
            "group": "Performance",
            "label": "Input Resolution",
            "default": 320,
            "options": [
                320,
                640
            ],
            "description": "320 fits fully on TPU (~4ms), 640 partially on CPU (~20ms)"
        },
        {
            "name": "tpu_device",
            "type": "select",
            "group": "Performance",
            "label": "TPU Device",
            "default": "auto",
            "options": [
                "auto",
                "0",
                "1",
                "2",
                "3"
            ],
            "description": "Which Edge TPU to use — auto selects first available"
        },
        {
            "name": "clock_speed",
            "type": "select",
            "group": "Performance",
            "label": "TPU Clock Speed",
            "default": "standard",
            "options": [
                "standard",
                "max"
            ],
            "description": "Max is faster but runs hotter — needs active cooling for sustained use"
        }
    ],
    "description": "Google Coral Edge TPU — real-time object detection natively via Windows WSL",
    "capabilities": {
        "live_detection": {
            "script": "scripts\/detect.py",
            "description": "Real-time object detection on live camera frames via Edge TPU inside WSL"
        }
    },
    "requirements": {
        "platforms": [
            "windows"
        ]
    }
}

Coral TPU Object Detection (Windows WSL)

Real-time object detection natively utilizing the Google Coral Edge TPU accelerator on your local hardware via Windows Subsystem for Linux (WSL). Detects 80 COCO classes (person, car, dog, cat, etc.) with ~4ms inference on 320x320 input.

Requirements

Google Coral USB Accelerator (USB 3.0 port recommended)
WSL2 installed and running on Windows
usbipd-win installed on the Windows host

How It Works

┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI on Windows)                          │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ WSL Container / Environment   │       │
│              │   detect.py                   │       │
│              │   ├─ loads _edgetpu.tflite     │       │
│              │   ├─ reads frame from disk     │       │
│              │   └─ runs inference on TPU    │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ usbipd-win bridge to WSL                  │
└─────────────────────────────────────────────────────┘

Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ workspace
Sends frame event via stdin JSONL to the WSL Python instance
detect.py invokes PyCoral and executes natively on the mapped USB Edge TPU inside Linux
Returns detections event via stdout JSONL back to Windows Host

Performance

Input Size	Inference	On-chip	Notes
320x320	~4ms	100%	Fully on TPU, best for real-time
640x640	~20ms	Partial	Some layers on CPU (model segmented)

Cooling: The USB Accelerator aluminum case acts as a heatsink. If too hot to touch during continuous inference, it will thermal-throttle. Consider active cooling or clock_speed: standard.

Installation

Windows (WSL)

Run deploy.bat — this will:

Verify usbipd is installed and bind the 18d1:9302 and 1a6e:089a Edge TPU hardware IDs.
Setup a Python virtual environment exclusively within WSL.
Install the Edge TPU libraries and dependencies within the WSL boundary.
Auto-attach the device using usbipd seamlessly during invocation.