AI agent skill · by @SharpAI

Yolo Detection 2026 Coral Tpu Macos

Google Coral Edge TPU — real-time object detection natively (macOS / Linux)

Archived 2026-10-23 · Source updated 2026-09-17 · License: MIT. Check the original repository for the latest version.

SKILL.md instructions

# Coral TPU Object Detection

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

## Requirements

- Python 3.9–3.13

## How It Works

```
┌─────────────────────────────────────────────────────┐
│ Host (Aegis-AI)                                     │
│   frame.jpg → /tmp/aegis_detection/                 │
│   stdin  ──→ ┌──────────────────────────────┐       │
│              │ Native Python Environment     │       │
│              │   detect.py                   │       │
│              │   ├─ loads _edgetpu.tflite     │       │
│              │   ├─ reads frame from disk     │       │
│              │   └─ runs inference on TPU    │       │
│   stdout ←── │   → JSONL detections          │       │
│              └──────────────────────────────┘       │
│   USB ──→ Native System USB / edgetpu drivers       │
└─────────────────────────────────────────────────────┘
```

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

## Platform Setup

### Linux
```bash
# Uses the official apt-get google-coral packages natively
./deploy.sh
```

### macOS 
```bash
# Downloads and installs the libedgetpu OS payload framework inline
./deploy.sh
```

> **Important Deployment Notice**: The updated `deploy.sh` script will natively halt execution and prompt you securely for your OS `sudo` password to securely register the USB drivers (`libedgetpu`) system-wide. If you refuse the prompt, it gracefully outputs the exact terminal instructions for you to configure it manually.

## 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`.

## Protocol

Same JSONL as `yolo-detection-2026`:

### Skill → Aegis (stdout)
```jsonl
{"event": "ready", "model": "yolo26n_edgetpu", "device": "coral", "format": "edgetpu_tflite", "tpu_count": 1, "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": 4.1, "p50": 3.9, "p95": 5.2}}}
```

### Bounding Box Format
`[x_min, y_min, x_max, y_max]` — pixel coordinates (xyxy).

## Installation

### Linux / macOS
```bash
./deploy.sh
```

The deployer builds the local Python virtual environment and installs the Edge TPU runtime. No Docker required.