Mainly because YOLOv8 is well-supported by the Rockchip/RKNN toolchain.

The goal here was an end-to-end RK3588S pipeline rather than comparing detector families: training/export, ONNX graph fixing, INT8 RKNN conversion, C++ postprocessing, and runtime inference across the 3 NPU cores. YOLOv8 has known-good export paths and Rockchip examples, so it was the most practical baseline.

Newer YOLO versions may be possible, but usually require more work around RKNN export compatibility.