基于深度学习的猪肉新鲜度分级设计与实现

Cong Wang, Cheng Lv, Run Li, Panpan Wang, Xiaodong Wang, A. Zhao, S. Jin, Bing Han, Shan Lu
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引用次数: 2

摘要

中国是世界上最大的猪肉生产和消费国,随着人们生活水平的提高和消费升级,人们对生鲜猪肉等生鲜产品的需求更加强烈。随着近两年非洲猪瘟和新冠肺炎疫情在中国的爆发,猪肉冷链运输将取代生猪成为猪肉供应链的主要模式。深度学习作为机器学习最重要的分支之一,近年来发展迅速,引起了国内外的广泛关注。为了提高猪肉新鲜度的实时检测,本文尝试了多种深度学习框架来实现猪肉新鲜度分类。本文根据TVB-N内容将猪肉新鲜度划分为5个等级,并对拍摄的图片进行不同深度学习网络的训练,包括VGG、GoogLeNet和RestNet。在分析了每个网络的训练情况后,吸收了不同网络的优点,构建了一种新的改进神经网络来预测猪肉新鲜度。最终的分类准确率达到97%,表明这是一种非常高效、准确的猪肉新鲜度分类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of Pork Freshness Grading Based on Deep Learning
China is the world's largest pork production and consumption country, with the improvement of people's living standards and consumption upgrade, people's demand for fresh pork and other fresh products is stronger. With the outbreak of African Swine Fever and COVID-19 in China in the past two years, cold chain transportation of pork will replace live pigs as the main mode of pork supply chain. As one of the most important branches of machine learning, deep learning has developed rapidly in recent years and attracted extensive attention at home and abroad. In order to improve the real-time detection of pork freshness, this paper experimented with a variety of deep learning frameworks to achieve pork freshness classification. In this paper, pork freshness is divided into 5 levels according to TVB-N content, and the pictures taken are trained by different deep learning networks, including VGG, GoogLeNet and RestNet. After analyzing the training situation of each network, the advantages of different networks are absorbed and a new improved neural network is built to predict pork freshness. The final classification accuracy reached 97%, Indicating that this is a very efficient and accurate pork freshness classification method.
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