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Refrigerators and Heat Pumps

Refrigerators and Heat Pumps

Refrigerators or heat pumps are heat engines operating in a reverse direction. For a refrigerator, the focus is on removing heat from a specific area, whereas, for a heat pump, the focus is on dumping heat into one particular area. A refrigerator (or heat pump) absorbs heat Qc from the cold reservoir at Kelvin temperature Tc and discards heat Qh to the hot reservoir at Kelvin temperature Th, while work W is done on the engine’s working substance.
A household refrigerator removes heat from...

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Related Experiment Video

Updated: Jun 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Robust deep-learning based refrigerator food recognition.

¹Advanced Technology Research Institute, Kyocera Corporation, Yokohama, Japan.

Frontiers in Artificial Intelligence

|December 19, 2024

Summary

This summary is machine-generated.

This study enhances artificial intelligence (AI) food recognition in smart refrigerators by improving the YOLACT model and using advanced data augmentation. The new method achieves higher accuracy in real-world conditions, aiding food waste reduction.

Keywords:

data augmentation deep learning feature pyramid network food management food recognition internet of Things

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Area of Science:

Computer Vision
Artificial Intelligence
Food Science

Background:

Smart refrigerators offer potential for automated food management.
Current AI food identification systems face challenges with varying distances, occlusions, and complex backgrounds.
Limited practical applicability hinders widespread adoption in household environments.

Purpose of the Study:

To improve the accuracy and robustness of AI-based food identification in smart refrigerators.
To address limitations in existing recognition systems concerning real-world variability.
To enhance the practical utility of automatic identification for household applications like food waste management.

Main Methods:

Enhanced the YOLACT model's Feature Pyramid Network (FPN) with an additional layer for nuanced feature capture.
Developed a two-stage data augmentation technique to simulate diverse conditions (distortion, occlusion, varied backgrounds, handheld scenarios).
Evaluated the improved model on a custom dataset and compared performance against existing research.

Main Results:

The enhanced model demonstrated significantly improved recognition rates on both typical and challenging real-world images.
The proposed data augmentation effectively simulated diverse environmental and object conditions.
The approach showed superior performance compared to previous methods in comparative analyses.

Conclusions:

The study presents a more effective AI approach for automatic food identification in smart refrigerators.
The enhanced model and data augmentation strategy overcome key limitations of prior systems.
This advancement holds promise for reducing household food waste and broader applications of automatic identification.