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

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

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                  │
└─────────────────────────────────────────────────────┘

1. Aegis writes camera frame JPEG to shared /tmp/aegis_detection/ workspace
2. Sends frame event via stdin JSONL to the WSL Python instance
3. detect.py invokes PyCoral and executes natively on the mapped USB Edge TPU inside Linux
4. 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:

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