I built this while trying to understand how much of the RK3588S vision pipeline could be kept off the CPU.

The main trick is not the YOLO model itself, but the pipeline structure: MIPI capture through the ISP, resize/color conversion through RGA, and YOLOv8n inference through all 3 NPU cores with one RKNN context per core. With a 3-thread inference pool the pipeline goes from ~31 FPS to the OS08A10 camera’s 46 FPS ceiling.

The memory footprint is also small: roughly 137–152 MB RSS for one 1080p stream, using a fixed preallocated buffer pool rather than per-frame allocations. Two streams are roughly 276–304 MB RSS.

The repo also has a multi-process side of the pipeline: detections are published over Unix-domain sockets to tracking, temporal features, a presence FSM, and an optional Qwen2.5-0.5B summary step. For the LLM step, the camera pipeline can temporarily blackout/resume so RKLLM gets the whole NPU.

I split the work into three repos:

- runtime dual-stream YOLOv8n RK3588S pipeline: https://github.com/alebal123bal/khadas_yolov8n_multithread

- train/export/INT8 RKNN conversion for YOLOv8/YOLOv5: https://github.com/alebal123bal/RKNN_TRAIN_YOLO

- Qwen on RK3588S, via RKLLM/NPU or llama.cpp/CPU: https://github.com/alebal123bal/RKLLM_LLAMA_QWEN

The demo class is UAV/drone, but this is meant as a general edge-inference pipeline example, not an operational/surveillance/defense system.