วันอังคารที่ 2 สิงหาคม พ.ศ. 2565

Raspberry pi YOLO Object detection with Intel Neural compute stick and OpenVINO

Raspberry pi YOLO Object detection with Intel Neural compute stick and OpenVINO 

Hardware · Raspberry Pi Board (4B ) · Intel Neural Compute Stick 2 · SD Card 32GB · 5V DC. 2A Power Supply Software · OS Raspbian 10 ( Buster ) · Python 3.7.3 · OpenVINO Toolkit 2020.3 · OpenCV 4.0.0

What is a YOLO object detector?

When it comes to deep learning-based object detection, there are three primary object detectors you’ll encounter:

  • R-CNN and their variants, including the original R-CNN, Fast R- CNN, and Faster R-CNN
  • Single Shot Detector (SSDs)
  • YOLO
First introduced in 2015 by Redmon et al., their paper, You Only Look Once: Unified, Real-Time Object Detection, details an object detector capable of super real-time object detection, obtaining 45 FPS on a GPU.

You Only Look Once: Unified, Real-Time Object Detection

https://arxiv.org/pdf/1506.02640v3.pdf


YOLOv3 improved on the YOLOv2 paper and both Joseph Redmon and Ali Farhadi, the original authors, contributed.
Together they published YOLOv3: An Incremental Improvement

The original YOLO papers were are hosted here

Author: Joseph Redmon and Ali Farhadi
Released: 8 Apr 2018

We’ll be using YOLOv3 in this blog post, in particular, YOLO trained on the COCO dataset.

The COCO dataset consists of 80 labels.

YOLOv3-416


Video Inference Performance    3 FPS.




YOLOv3-tiny-416


Video Inference Performance    7 FPS.




Compare Performance



Reference

Raspberry pi OpenVINO with Intel Movidius ( Neural Compute Stick )


Install OpenVINO™ toolkit for Raspbian* OS https://docs.openvino.ai/latest/openvino_docs_install_guides_installing_openvino_raspbian.html


Adun Nantakaew อดุลย์ นันทะแก้ว 081-6452400
LINE : adunnan

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