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And the related works
In this report we use YOLO.
full name is you only look once.
traditional methods like CNN, RNN is effective, but not good enough.
While these models exhibit reasonably high accuracy,
they were also come with complexity and slower inference times.
Over time, we have new solutions that could do the job quicker and more efficiently. that is YOLO.
YOLO is introduced in 2016 and it soon become the fastest model,
Sacrifice a little bit of accuracy in exchange for extremely fast inference speed.
YOLO is the fastest than other one-state models.
in this research, we use YOLOv8, which is the state-of-the-art (SOTA) models.
It used CSPDarkNet as backbone.
And PAFPN as neck.
And head part it used YOLOv8 decoupled head.
It is faster and more accurate.
Because we need to recognize more and more detail, the traditional methods have to use more hardware cost.
So we choose to build small models base on YOLO and make each of model can be deploy decentralized.
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IEEE/ICACT20240034 Slide.06
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