基于YOLO算法的瓶子目标检测

2022 4th International Conference on Cybernetics and Intelligent System (ICORIS) Pub Date : 2022-10-08 DOI:10.1109/ICORIS56080.2022.10031554

Fathi Sei Pahangai Akbar, Steven Yanuar Prasetyo Ginting, Giovanna Cheryl Wu, Said Achmad, Rhio Sutoyo

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引用次数: 1

摘要

物体识别是当今数字时代经常使用的工具。物体识别可以识别一个物体。然而，我们不能识别每一个单独的对象，除非对象已被标记和研究的机器。我们在这项研究中的目标是创建一个可以使用YOLOv3和COCO数据集检测瓶子的程序，以及一个易于实践的简单架构模型。在本研究中，我们将使用YOLO，并且取数据集，或者可以识别的对象仅为COCO数据集中的对象。然后我们对自己收集的瓶子进行对象识别，作为真实案例数据测试。我们发现，在相同的数据集上，YOLOv3比YOLOv2更好地检测对象。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Object Detection on Bottles Using the YOLO Algorithm

Object recognition is a tool that is often used in today's digital era. Object recognition can identify an object. However, we cannot identify every single object unless the object has been tagged and studied by the machine. Our goal in this research is to create a program that can detect bottles with the YOLOv3 and COCO datasets and a simple architectural model that can be easily practiced. In this research, we will use YOLO, and the dataset is taken, or the objects that can be identified are only objects in the COCO dataset. Then we do object recognition of the bottles that we collect ourselves as a real case data test. We found that YOLOv3 is better at detecting objects than YOLOv2 with the same dataset.

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2022 4th International Conference on Cybernetics and Intelligent System (ICORIS)

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