Agent Skills
› SharpAI/DeepCamera
› yolo-detection-2026-coral-tpu-macos
yolo-detection-2026-coral-tpu-macos
GitHub基于Google Coral Edge TPU的实时物体检测技能，支持macOS/Linux原生部署。通过USB连接TPU加速器，以JSONL协议接收图像帧并返回检测结果与性能统计，适用于边缘计算场景。
Trigger Scenarios
需要进行实时物体检测
使用Coral Edge TPU硬件加速推理
Install
npx skills add SharpAI/DeepCamera --skill yolo-detection-2026-coral-tpu-macos -g -y
SKILL.md
Frontmatter
{
"icon": "assets\/icon.png",
"name": "yolo-detection-2026-coral-tpu-macos",
"entry": "scripts\/detect.py",
"mutex": "detection",
"deploy": {
"linux": "deploy.sh",
"macos": "deploy.sh"
},
"runtime": "python",
"version": "1.0.0",
"category": "detection",
"parameters": [
{
"name": "auto_start",
"type": "boolean",
"group": "Lifecycle",
"label": "Auto Start",
"default": false,
"description": "Start this skill automatically when Aegis launches"
},
{
"max": 1,
"min": 0.1,
"name": "confidence",
"type": "number",
"group": "Model",
"label": "Confidence Threshold",
"default": 0.5,
"description": "Minimum detection confidence — lower than GPU models due to INT8 quantization"
},
{
"name": "classes",
"type": "string",
"group": "Model",
"label": "Detect Classes",
"default": "person,car,dog,cat",
"description": "Comma-separated COCO class names (80 classes available)"
},
{
"name": "fps",
"type": "select",
"group": "Performance",
"label": "Processing FPS",
"default": 5,
"options": [
0.2,
0.5,
1,
3,
5,
15
],
"description": "Frames per second — Edge TPU handles 15+ FPS easily"
},
{
"name": "input_size",
"type": "select",
"group": "Performance",
"label": "Input Resolution",
"default": 320,
"options": [
320,
640
],
"description": "320 fits fully on TPU (~4ms), 640 partially on CPU (~20ms)"
},
{
"name": "tpu_device",
"type": "select",
"group": "Performance",
"label": "TPU Device",
"default": "auto",
"options": [
"auto",
"0",
"1",
"2",
"3"
],
"description": "Which Edge TPU to use — auto selects first available"
},
{
"name": "clock_speed",
"type": "select",
"group": "Performance",
"label": "TPU Clock Speed",
"default": "standard",
"options": [
"standard",
"max"
],
"description": "Max is faster but runs hotter — needs active cooling for sustained use"
}
],
"description": "Google Coral Edge TPU — real-time object detection natively (macOS \/ Linux)",
"capabilities": {
"live_detection": {
"script": "scripts\/detect.py",
"description": "Real-time object detection on live camera frames via Edge TPU"
}
},
"requirements": {
"platforms": [
"linux",
"macos"
]
}
}
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 │
└─────────────────────────────────────────────────────┘
- Aegis writes camera frame JPEG to shared
/tmp/aegis_detection/workspace - Sends
frameevent via stdin JSONL to the local Python instance detect.pyinvokes PyCoral and executes natively on the mapped USB Edge TPU- Returns
detectionsevent via stdout JSONL
Platform Setup
Linux
# Uses the official apt-get google-coral packages natively
./deploy.sh
macOS
# 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.
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)
{"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
./deploy.sh
The deployer builds the local Python virtual environment and installs the Edge TPU runtime. No Docker required.
Version History
- 2264fcb Current 2026-08-20 16:02
