YOLOv8 Multi-stream Recognition
Multi-stream Video Object Detection with YOLOv8 on RK3588 NPU
Features
- Multi-stream Processing: Supports 9+ video streams simultaneously
- 3-core NPU Acceleration: Fully utilizes RK3588's 3 NPU cores
- Pipeline Architecture: Decode → NPU Inference → Post-processing → Display
- Real-time Display: Grid display with frame rate statistics (video FPS + inference FPS)
- Letterbox Preprocessing: Maintains aspect ratio to ensure detection accuracy
Installation
Clone Project Repository
Device
git clone https://github.com/ZIFENG278/RK3588-Multi-Stream-YOLOv8-Detection.git
Install Dependencies
tip
Please install dependencies in a Python virtual environment
Device
pip3 install rknn-toolkit-lite2 opencv-python
Usage
Basic Run (9 streams, 3 cores)
tip
--video-dir: Please specify the folder containing video files
Device
python3 main_rknn_pipeline.py --video-dir ./video
No Display Mode (suitable for testing)
Device
python3 main_rknn_pipeline.py --video-dir ./video --no-display --max-frames 100
Custom Parameters
Device
python3 main_rknn_pipeline.py \
--num-streams 6 \
--num-cores 3 \
--model yolov8n-i8-3588.rknn \
--video-dir video \
--conf-threshold 0.5 \
--iou-threshold 0.45
Command Line Parameters
| Parameter | Default | Description |
|---|---|---|
--num-streams | 9 | Number of video streams |
--num-cores | 3 | NPU core count (1-3) |
--model | yolov8n-i8-3588.rknn | Model file path |
--video-dir | video | Video directory |
--conf-threshold | 0.4 | Confidence threshold |
--iou-threshold | 0.45 | NMS IoU threshold |
--no-display | False | Disable display |
--max-frames | None | Limit number of frames |
Performance Reference
Test Conditions
Device
python main_rknn_pipeline.py \
--num-streams 9 \
--num-cores 3 \
--model yolov8n-i8-3588.rknn \
--video-dir video \
--max-frames 1000
| Hardware | Memory | Video Count | NPU Cores | Model | Quantization | Frames |
|---|---|---|---|---|---|---|
| ROCK5B+ | 8GB | 9 | 3 | yolov8n | INT8 | 1000 |
Test Results
| Stage | Time (avg ms) |
|---|---|
| Decode | 2.24 |
| Preprocess | 2.00 |
| NPU Inference | 25.77 |
| Post-process | 15.66 |
| Draw | 1.43 |
| Total | 47.10 |