Google Coral Edge TPU — real-time object detection natively (macOS / Linux)
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.
┌─────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────┘
/tmp/aegis_detection/ workspaceframe event via stdin JSONL to the local Python instancedetect.py invokes PyCoral and executes natively on the mapped USB Edge TPUdetections event via stdout JSONL# Uses the official apt-get google-coral packages natively
./deploy.sh
# Downloads and installs the libedgetpu OS payload framework inline
./deploy.sh
Important Deployment Notice: The updated
deploy.shscript will natively halt execution and prompt you securely for your OSsudopassword 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.
| 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.
Same JSONL as yolo-detection-2026:
{"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}}}
[x_min, y_min, x_max, y_max] — pixel coordinates (xyxy).
./deploy.sh
The deployer builds the local Python virtual environment and installs the Edge TPU runtime. No Docker required.