An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning

Liwei Chen

doi:10.47297/taposatWSP2633-456918.20250612

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An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning

6卷第12期

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An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning
An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning
James Cook University in Singapore, Information Technology, 387380, Singapore
2025年6卷第12期页码：85-92
纸质出版：2025-12-30
DOI：10.47297/taposatWSP2633-456918.20250612
Accepted：

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Liwei Chen. (2025). An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning. 科技理论与实践（英文版）, 6(12), 85-92.

Chen Liwei.An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning[J].Theory and Practice of Science and Technology,2025,06(12):85-92.
Liwei Chen. (2025). An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning. 科技理论与实践（英文版）, 6(12), 85-92. DOI： 10.47297/taposatWSP2633-456918.20250612.

Chen Liwei.An AR-Assisted Food Ingredient Recognition System for Smart Refrigerators Using Deep Learning[J].Theory and Practice of Science and Technology,2025,06(12):85-92. DOI： 10.47297/taposatWSP2633-456918.20250612.

摘要

Abstract

Food waste at the household level has become a significant global issue

largely due to inefficient food management and lack of awareness of stored ingredients. This paper proposes an Augmented Reality (AR)-based food recognition system designed for household refrigerators. The system integrates lightweight deep learning-based food recognition with real-time AR visualization to improve food visibility and usage efficiency. Using a lightweight CNN-based object detection model (YOLOv8) with real-time AR visualization

the proposed system identifies common refrigerator food items under challenging conditions such as low lighting

occlusion

and clutter. Experimental results demonstrate that the system can accurately recognize and localize multiple food items in real time

providing a practical solution for reducing household food waste and enhancing smart kitchen applications.

关键词

Keywords

references

Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food-101: Mining discriminative components with random forests. In European Conference on Computer Vision (ECCV 2014) (pp. 446–461). Springer.

Bokhari, M. (2023). Computer vision for determination of fridge contents (Master's thesis). University of Passau, Germany.

Honee, D., Hurst, W., & Luttikhold, A. J. (2022). Harnessing augmented reality for increasing awareness of food waste. Augmented Human Research, 7(1), Article 5.

Koporec, J., Pers, J., & Kristan, M. (2019). Deep learning performance in the presence of significant occlusions. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2019) (pp. 1–8). IEEE.

Morris, S. V. (2020). Inventory management of refrigerator produce bins using computer vision (Master's thesis). University of Arkansas, United States.

Ultralytics. (2023). YOLOv8: Next-generation object detection and segmentation models.