探索基于深度学习的非洲社会文化食物识别系统模型

IF 4.3 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Grace Ataguba, Mona Alhasani, James Daniel, Emeka Ogbuju, Rita Orji
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引用次数: 0

摘要

食物识别作为食物计算的一个领域,极大地促进了人们的饮食决策和饮食习俗。我们利用迁移学习等深度学习模型,设计并评估了一款用于非洲食物识别的社会文化应用程序。深度学习模型具有多个处理层,这使其在图像识别方面具有很强的鲁棒性。基于深度学习模型的这种能力,我们在本研究中对其进行了探索。我们从三个非洲国家共收集了 3142 个食品图像数据集:尼日利亚、加纳和喀麦隆。利用这些数据集,我们开发并训练了一个识别非洲食品的深度学习模型。该模型的测试准确率达到 94.5%。该模型被进一步部署到一款食品识别应用程序中。为了评估该应用的预测能力,我们招募了 16 名参与者,对他们进行了访谈,随后让他们在野外使用该应用 7 天。在对应用程序和人类识别能力的比较评估中,我们发现应用程序能够识别71%由参与者生成并通过应用程序进行测试的食物图像实例,而人类评估者(参与者)只能识别56%的食物数据集。参与者大多能识别出本国的一些食品。此外,参与者还为应用程序提出了一些设计建议。有鉴于此,我们为社会文化食物识别系统的研究人员和设计人员提供了一些设计建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploring Deep Learning–Based Models for Sociocultural African Food Recognition System

Exploring Deep Learning–Based Models for Sociocultural African Food Recognition System

Food recognition, a field under food computing, has significantly promoted people’s dietary decision-making and culinary customs. We present the design and evaluation of a sociocultural app for African food recognition using deep learning models such as transfer learning. Deep learning models have multiple processing layers that make them robust in image recognition. Based on this capability of deep learning models, we explored them in this study. A total of 3142 food image datasets were collected from three African countries: Nigeria, Ghana, and Cameroon. Using the datasets, we developed and trained a deep learning model for recognizing African foods. The model attained a test accuracy of 94.5%. The model was further deployed in a food recognition app. To evaluate the predictive ability of the app, we recruited 16 participants who were interviewed and subsequently used the app in the wild for 7 days. In a comparative evaluation between the app and human recognition capabilities, we found that the app recognized 71% of the instances of food images generated by the participants and tested with the app, while the human evaluators (participants) could only recognize 56% of the food datasets. Participants were mostly able to recognize some foods from their own country. Furthermore, participants suggested some design features for the app. In view of this, we offer design recommendations for researchers and designers of sociocultural food recognition systems.

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来源期刊
Human Behavior and Emerging Technologies
Human Behavior and Emerging Technologies Social Sciences-Social Sciences (all)
CiteScore
17.20
自引率
8.70%
发文量
73
期刊介绍: Human Behavior and Emerging Technologies is an interdisciplinary journal dedicated to publishing high-impact research that enhances understanding of the complex interactions between diverse human behavior and emerging digital technologies.
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