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
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
