This post shows a walkthrough of the ARA2 Vision Examples demo and its multi-stream YOLOv8 object detection application.
The ara2-vision-examples demo provides vision AI examples for NXP i.MX platforms using Ara240 DNPU acceleration. It demonstrates real-time video processing with AI/ML inference capabilities such as object detection, classification, pose estimation, and semantic segmentation.
This walkthrough focuses on the Go Point launch of the application, which uses GStreamer to process up to eight simultaneous video streams, run YOLOv8 object detection on each stream, and display the results in a single mosaic view.
Run the application with the default settings:
multistream_yolov8
Run with a specific number of streams:
multistream_yolov8 -s 4
Select a different YOLOv8 model:
multistream_yolov8 -s 4 --model yolov8s
Run eight streams for maximum throughput:
multistream_yolov8 -s 8 --sync false
Enable synchronized playback:
multistream_yolov8 -s 4 --sync true
In the attached video, it is shown how to launch the application from GoPoint, configure the number of streams, select different YOLOv8 models, and view the object detection results in the mosaic display.
Ara-Vision-Examples is the reference application showcasing edge vision AI on the Ara240 Discrete NPU (DNPU). It runs real-time object detection, classification, pose estimation and semantic segmentation over single or multiple (up to eight) video streams, rendering bounding boxes, labels and confidence scores.
ARA2 Vision Examples repository:
https://github.com/nxp-imx-support/ara2-vision-examples
Multi-stream YOLOv8 README:
https://github.com/nxp-imx-support/ara2-vision-examples/blob/main/tasks/object-detection/yolov8n/mul...