论文标题
基于机器学习的实时垃圾食品识别系统
A Real-time Junk Food Recognition System based on Machine Learning
论文作者
论文摘要
$ $由于饮食习惯不良，人类可能会被摧毁。人们一直在寻找美味的食物，垃圾食品是最常见的来源。结果，我们的饮食模式正在转移，我们比以往任何时候都更倾向于垃圾食品，这对我们的健康不利，增加了我们获得健康问题的风险。机器学习原理是在我们生活的各个方面都应用的，其中之一是通过图像处理的对象识别。但是，由于食物在性质上有所不同，因此此过程至关重要，而诸如ANN，SVM，KNN，PLS等的传统方法将导致较低的精度率。所有这些问题都被深层神经网络击败。在这项工作中，我们创建了一个来自20个垃圾食品分类的10,000个数据点的新数据集，以试图识别垃圾食品。数据集中的所有数据都是使用Google搜索引擎收集的，该引擎被认为在各种方面都是独一无二的。使用卷积神经网络（CNN）技术实现了该目标，该技术众所周知。在整个研究中，我们达到了98.05 \％的准确率，这令人满意。此外，我们根据现实生活事件进行了测试，结果非常非凡。我们的目标是将这项研究提高到一个新的水平，以便将其应用于未来的研究。我们的最终目标是创建一个系统，该系统鼓励人们避免吃垃圾食品并具有健康意识。 \关键字{机器学习\和垃圾食品\以及对象检测\和yolov3 \和自定义食品数据集。}
$ $As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we're gravitating toward junk food more than ever, which is bad for our health and increases our risk of acquiring health problems. Machine learning principles are applied in every aspect of our lives, and one of them is object recognition via image processing. However, because foods vary in nature, this procedure is crucial, and traditional methods like ANN, SVM, KNN, PLS etc., will result in a low accuracy rate. All of these issues were defeated by the Deep Neural Network. In this work, we created a fresh dataset of 10,000 data points from 20 junk food classifications to try to recognize junk foods. All of the data in the data set was gathered using the Google search engine, which is thought to be one-of-a-kind in every way. The goal was achieved using Convolution Neural Network (CNN) technology, which is well-known for image processing. We achieved a 98.05\% accuracy rate throughout the research, which was satisfactory. In addition, we conducted a test based on a real-life event, and the outcome was extraordinary. Our goal is to advance this research to the next level, so that it may be applied to a future study. Our ultimate goal is to create a system that would encourage people to avoid eating junk food and to be health-conscious. \keywords{ Machine Learning \and junk food \and object detection \and YOLOv3 \and custom food dataset.}
