Qengineering/YoloV10-NPU

YoloV10 NPU for the RK3566/68/88

34
/ 100
Emerging

This project helps you identify and locate objects within images or video streams using powerful AI models. It takes live camera feeds or static image files as input and outputs bounding boxes and labels for detected objects, such as cars, people, or specific items. It is designed for engineers and developers working on embedded systems that use Rockchip's RK3566, RK3568, or RK3588 NPUs to create efficient, real-time object detection applications.

No commits in the last 6 months.

Use this if you need high-performance, real-time object detection on specialized Rockchip NPU hardware for embedded computer vision applications.

Not ideal if you are looking for a general-purpose object detection solution for desktop GPUs, cloud environments, or different embedded hardware platforms.

embedded-vision real-time-object-detection edge-ai industrial-automation robotics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 12 / 25

How are scores calculated?

Stars

20

Forks

3

Language

C++

License

BSD-3-Clause

Last pushed

Jun 19, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/Qengineering/YoloV10-NPU"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.