// Google Coral Edge TPU — real-time object detection natively (macOS / Linux)
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| name | yolo-detection-2026-coral-tpu-macos |
| description | Google Coral Edge TPU — real-time object detection natively (macOS / Linux) |
| version | 1.0.0 |
| icon | assets/icon.png |
| entry | scripts/detect.py |
| deploy | {"linux":"deploy.sh","macos":"deploy.sh"} |
| runtime | python |
| requirements | {"platforms":["linux","macos"]} |
| 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"}} |
| category | detection |
| mutex | 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.
┌─────────────────────────────────────────────────────┐
│ 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.