Tezislər / Theses

THE 3
rd
 INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS 
dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
174
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Speed: Because it can anticipate objects in real-time, this approach 
enhances detection speed. 
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High accuracy: YOLO is a prediction strategy that yields accurate 
findings while minimizing background mistakes. 
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Learning capabilities: The method has great learning capabilities, allowing 
it to learn object representations and apply them to object detection. 
I utilize a Raspberry Pi 4 Model B as a mobile platform. The Raspberry 
Pi is a single-board computer, which means that the microprocessor, memory, 
wireless radios, and ports are all positioned on a single board. Because the 
Pi is a Linux computer, it can technically do all of the functions of a Linux 
computer and being used for OBJECT DETECTION. Unlike most computers 
that have a built-in hard drive or SSD storage option, the Pi's operating 
system is loaded on a microSD card, which is also where you'll save all of 
your data because the board lacks any built-in storage. As the hardware part 
of our object detector, we used a Raspberry Pi 4 Model B and a Raspberry 
Pi Camera. We'll also need a microSD card with at least 32GB of storage 
space because creating OpenCV may be a memory-intensive process. 
You only look once (YOLO) model results (Figure 1):
References: 
[1] https://www.pyimagesearch.com/2020/01/27/yolo-and-tiny-yolo-object-detection-on-the-
raspberry-pi-and-movidius-ncs/ 
[2] https://medium.datadriveninvestor.com/object-detection-with-raspberry-pi-and-python-
bc6b3a1d4972 
[3] Albawi, Saad & Abed Mohammed, Tareq & ALZAWI, Saad. (2017). Understanding of a 
Convolutional Neural Network. 10.1109/ICEngTechnol.2017.8308186. 
[4] A. F. Joseph Redmon, "YOLOv3: An Incremental Improvement," University of Washington, 
2018. 

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