Edge Vision Engineering (Jetson, Pi, Coral)
Optimize and deploy real-time object detection models to edge hardware like Raspberry Pi, Jetson, and Coral.
$24
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Grok Bot+18 agentsEdge Vision Engineering (Jetson, Pi, Coral)
Example session with this skill installed
I need to run object detection for a parcel sorting robot on a Jetson Orin Nano. I need to detect boxes and bags at 30 FPS. Help me pick a model and export path.
- Read your context and instructions
- Compiled the edge vision engineering
I have evaluated the requirements for your parcel sorting robot. Given the 30 FPS target on a Jetson Orin Nano, the primary bottleneck will be the post-processing and memory bandwidth during inference.
Edge Vision Deployment Plan: Jetson Orin Nano
| Component | Recommendation | Details |
|---|---|---|
| Model Architecture | YOLOv8n (Nano) | Optimized for high-throughput low-latency detection of small-to-medium objects. |
| Optimization Path | TensorRT | Native NVIDIA acceleration using FP16 precision for the best balance of speed/accuracy. |
| Export Format | .engine | Compile via trtexec or Python API with a workspace size of 4GB. |
| Latency Target | < 25ms | Target end-to-end (Capture -> Infer -> NMS) to ensure a stable 30 FPS buffer. |
| Integration | ROS 2 Topic | Publish vision_msgs/Detection2DArray to the local robot controller. |
Technical Implementation Notes
Quantization: Use FP16. INT8 is possible but requires a diverse calibration set of at least 100 images of your specific sorting facility lighting to avoid accuracy drops on plastic bags.
Preprocessing: Ensure hardware-accelerated decoding (NVDEC) if using a high-resolution camera stream to avoid CPU bottlenecks.
Privacy: Implement local processing only; if logging for debugging, apply a Gaussian blur to any shipping labels detected on the boxes.
Next steps
- Benchmark a pre-trained YOLOv8n model on the Orin Nano using the
onnx2tensorrtworkflow. - Capture 100 frames from the sorting line to create a validation set for precision/recall testing.
- Configure an MQTT or ROS 2 node to handle the detection output without streaming raw video.
Connects securely to your tools. The creator never sees your data.
About this skill
Measured latency, INT8 accuracy and privacy by design.
Run real-time object detection on edge hardware (Raspberry Pi 5 with AI accelerator, NVIDIA Jetson, Coral, a laptop GPU) for robots, drones, counting or monitoring - model choice, export (ONNX/TensorRT/TFLite), measured latency and accuracy, and privacy-preserving alerts. Use when the user wants a camera to detect or count objects locally, speed up a vision model on small hardware, or add vision to a robot or drone.
Includes an EXPERT.md reference (exact parameters, thresholds, failure diagnosis).
Acceptable use: civil and commercial applications only. Any military, paramilitary or weapon-related use is prohibited, as stated in the skill itself.
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