Raspberry pi YOLO Object Detection : yolov3-tiny-416
more info
http://raspberrypi4u.blogspot.com/202...
YOLOv3-tiny-416 Performance : 7.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 2020.3
· 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
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
https://pjreddie.com/darknet/yolo/
The original YOLO papers were are hosted here
https://pjreddie.com/darknet/yolo/
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.
Install OpenVINO™ toolkit for Raspbian* OS
https://docs.openvino.ai/latest/openv...
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