Food Image Recognition for Price Calculation using Convolutional Neural Network

Md. Jan Nordin, Ooi Wei Xin, Norshakirah Aziz
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引用次数: 6

Abstract

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the café of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Café, UTP with a trained CNN classification model at the backend.
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基于卷积神经网络的食品图像识别价格计算
这个项目试图解决在马来西亚小贩摊位的咖啡馆中普遍存在的经济米或混合米的食品价格不公平和不一致的问题。造成这一问题的主要原因是由于没有统一的食品价目表,导致混合大米的价格一直不明。因此,作者决定利用卷积神经网络(CNN)算法提出这个项目,并开发一个web应用程序,以减轻供应商的负担,并向买方提供所收取的食品价格的透明度。训练CNN模型对不同类型的食物进行分类。食品价格将存储在web应用程序的数据库中,以便在机器学习模型中使用识别的食品计算食品价格。该项目的结果是为Village 3 caf定制的web应用程序,UTP在后端使用经过训练的CNN分类模型。
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