A. Wibowo, Lusiana Lusiana, Tika Kartika Dewi
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引用次数: 0

摘要

水果是人体必须满足的营养需求之一。但注意,这些营养物质将从仍然新鲜的水果中获得。新鲜水果本身的定义是可以直接食用,不需要任何进一步加工的水果。有许多方法可以选择和区分新鲜水果和坏水果,一般可以直接观察。但随着时间的推移,还有其他几种方法可以利用现有的技术来观察水果的新鲜度。其中之一就是优化深度学习和机器学习。这个检测和分类系统是使用YOLOv5算法的深度学习方法创建的,该算法可以实时检测苹果,香蕉和橙子的类型。对于新鲜水果和腐烂水果,我们使用每一种水果的图像数据集,训练数据总共使用1200张图像,验证数据使用330张图像,测试数据使用6张图像。通过对训练数据、验证数据以及使用YOLOv5算法的测试数据进行测试,可以得出该检测方法能够一致性地识别物体,并且具有较高的准确率。这可以在准确率达到90%的准确度水平上得到证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementasi Algoritma Deep Learning You Only Look Once (YOLOv5) Untuk Deteksi Buah Segar Dan Busuk
Fruit is one of the nutritional needs for the body that must be met. But with a note, these nutrients will be obtained from fruit that is still fresh. The definition of fresh fruit itself is fruit that can be consumed directly and does not require any further processing. There are many ways to select and differentiate between fresh fruit and bad fruit and in general direct observations can be made. But over time, there are several other ways to observe fruit freshness using existing technology. Where one of them is by optimizing deep learning and machine learning. This detection and classification system was created using a deep learning method using the YOLOv5 algorithm which can detect in real-time the types of apples, bananas and oranges. We use image datasets for each of these fruits for fresh fruit and rotten fruit, a total of 1200 images for train data and 330 images for validation data and 6 images for test data. Based on the tests that have been carried out with training data, along with validation data, and test data using the YOLOv5 algorithm, it can be concluded that this detection method can recognize objects consistently with a high degree of accuracy. This can be proven at the level of accuracy which reaches an accuracy rate of 90%.
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