Raspberry pi Object Detection YOLOv4 vs. YOLOv4-tiny Compare

Performance Compare
YOLOv4 : 1.x FPS.
YOLOv4-tiny : 6.x FPS.

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 2021.3
· OpenCV 4.0.0

What is a YOLO object detection?
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

YOLOv4
With the original authors work on YOLO coming to a standstill, YOLOv4 was released by Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. The paper was titled YOLOv4: Optimal Speed and Accuracy of Object Detection

Author: Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao
Released: 23 April 2020

The original YOLO papers were are hosted here
Author: Joseph Redmon and Ali Farhadi
Released: 8 Apr 2018

We’ll be using YOLOv4 in this blog post, in particular, YOLO trained on the COCO dataset.
The COCO dataset consists of 80 labels.

Install OpenVINO™ toolkit for Raspbian* OS

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