Edge CV: YOLO Object Detection on RK3576 - 1

Edge CV: YOLO Object Detection on RK3576

Run YOLO 11 object detection locally on reComputer RK3576 with RKNN NPU acceleration. Choose nano, small, or medium model.

Beginner10minVision
rk3576yoloobject-detectionedge-ainpu

What It Does

Turn your reComputer RK3576 into a real-time object detector. YOLO 11 identifies people, vehicles, animals, and 80 object types in images or live video — all processed locally on your device.

Core Value

  • Three model sizes — Nano (fastest), Small (balanced), Medium (most accurate) to match your needs
  • Flexible input — upload images, process video files, or use a live USB camera feed
  • Standard REST API — simple HTTP endpoints for easy integration into any application
  • NPU accelerated — RKNN NPU delivers real-time detection on low-power hardware

Use Cases

ScenarioDescription
Security monitoringDetect people and vehicles in camera feeds
Retail analyticsCount customers and track foot traffic
Quality inspectionIdentify defects on production lines
Wildlife monitoringDetect and classify animals in outdoor cameras

Good to Know

  • Works without camera — upload images via API for detection
  • Supports custom class filtering via configuration file
  • Detection thresholds (confidence, NMS IOU) are adjustable at runtime
  • MJPEG video feed available for real-time browser preview

Integration Interfaces

http

Object detection prediction API (supports image upload, video, and realtime camera)

/api/models/yolo11/predict · Port: 8000 · Method: POST
{"predictions":[{"class":"person","confidence":0.92,"box":{"x1":100,"y1":200,"x2":300,"y2":500}}]}
http_stream

MJPEG video stream with detection bounding boxes overlay

/api/video_feed · Port: 8000 · Method: GET
http

Get/set detection thresholds (confidence, NMS IOU)

/api/config · Port: 8000 · Method: GET

Usage Requirements

network

Network connection for Docker image pull

rtsp

USB camera (optional, for realtime detection)

Deployment Options

Download & Install

Preset: RK3576 Object Detection {#rk3576_cv}

Deploy YOLO 11 object detection to your reComputer RK3576 with one click.

DevicePurpose
reComputer RK3576Runs YOLO 11 with RKNN NPU acceleration
USB Camera (optional)Real-time video detection input

What you'll get:

  • YOLO 11 detection API running locally on your device
  • Choose from 3 model sizes: Nano (fastest), Small (balanced), Medium (most accurate)
  • REST API for image/video detection + MJPEG live video feed
  • Supports 80 COCO object classes out of the box

Requirements: RK3576 device with SSH access + Docker installed

Step 1: Deploy YOLO 11 {#deploy_cv type=docker_deploy required=true config=devices/rk3576.yaml}

Deploy the object detection container to your RK3576 device.

Target: Remote Deployment {#rk3576_remote type=remote config=devices/rk3576.yaml default=true}

Deploy to your RK3576 over SSH with one click.

Wiring

  1. Connect RK3576 to the same network as your computer
  2. Plug in USB camera if you want real-time video detection
  3. Select the model size (Nano recommended for beginners)
  4. Fill in device IP, SSH username, and password
  5. Click Deploy

Deployment Complete

  1. The YOLO container is running on your RK3576
  2. Detection API: http://<device-ip>:8000/api/models/yolo11/predict
  3. Live video feed: http://<device-ip>:8000/api/video_feed (requires camera)
  4. Web preview: http://<device-ip>:8000 (if available)

Troubleshooting

IssueSolution
SSH connection failedVerify IP address, username, password
NPU not detectedEnsure device is RK3576 with RKNPU kernel module loaded
No camera detectedCheck USB camera is connected. Detection still works with image upload API
Image pull slowCheck network connection. Image is about 1-2GB

Step 2: Try Detection {#verify_cv type=image_predict}

Verify the detection service is working.

Mode: Image Detection {#image_mode config=devices/cv_image.yaml default=true}

Upload an image to test object detection.

Troubleshooting

IssueSolution
No detectionsTry an image with people or vehicles
Connection refusedWait 15-30 seconds for service to start

Mode: Live Video {#video_mode config=devices/cv_stream.yaml}

View live camera feed with detection bounding boxes (requires USB camera).

Troubleshooting

IssueSolution
Black screenCheck USB camera is connected
No video feedVerify MJPEG URL is correct

Deployment Complete

YOLO 11 object detection is running on your RK3576 device.

Image Upload Example

curl -X POST http://<device-ip>:8000/api/models/yolo11/predict \
  -F "file=@photo.jpg" \
  -F "conf=0.5"

Live Video Feed

Open in browser: http://<device-ip>:8000/api/video_feed

Adjust Detection Thresholds

curl -X POST http://<device-ip>:8000/api/config \
  -H "Content-Type: application/json" \
  -d '{"obj_thresh": 0.3, "nms_thresh": 0.5}'
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