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.