Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms
The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7660 |
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| author | Bryan Gonzalez Gonzalo Garcia Sergio A. Velastin Hamid GholamHosseini Lino Tejeda Gonzalo Farias |
| author_facet | Bryan Gonzalez Gonzalo Garcia Sergio A. Velastin Hamid GholamHosseini Lino Tejeda Gonzalo Farias |
| author_sort | Bryan Gonzalez |
| collection | DOAJ |
| description | The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for plate counting and content identification algorithm comparison, using standard evaluation metrics. The approach utilized the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. The model is trained on labeled image data, and its performance is assessed using a precision–recall curve at a confidence threshold of 0.5, achieving a mean average precision (mAP) of 0.873, indicating robust overall performance. The weight estimation procedure combines computer vision techniques to measure food volume using both RGB and depth cameras. Subsequently, density models specific to each food type are applied to estimate the detected food weight. The estimation model’s parameters are calibrated through experiments that generate volume-to-weight conversion tables for different food items. Validation of the system was conducted using rice and chicken, yielding error margins of 5.07% and 3.75%, respectively, demonstrating the feasibility and accuracy of the proposed method. |
| format | Article |
| id | doaj-art-d5e4cd1d9dd24768835be38b9ec68af4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d5e4cd1d9dd24768835be38b9ec68af42025-08-20T02:38:45ZengMDPI AGSensors1424-82202024-11-012423766010.3390/s24237660Automated Food Weight and Content Estimation Using Computer Vision and AI AlgorithmsBryan Gonzalez0Gonzalo Garcia1Sergio A. Velastin2Hamid GholamHosseini3Lino Tejeda4Gonzalo Farias5Escuela de Ingenieria Electrica, Pontificia Universidad Catolica de Valparaıso, Valparaíso 2340025, ChileCollege of Engineering, Virginia Commonwealth University, Richmond, VA 23220, USASchool of Electronic Engineering and Computer Science, Queen Mary University of London, London SE1 9RT, UKSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSourcing, ChileEscuela de Ingenieria Electrica, Pontificia Universidad Catolica de Valparaıso, Valparaíso 2340025, ChileThe work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining hall setting. An RGB camera is employed to capture the tray delivery process in a self-service restaurant, providing test images for plate counting and content identification algorithm comparison, using standard evaluation metrics. The approach utilized the YOLO architecture, a widely recognized deep learning model for object detection and computer vision. The model is trained on labeled image data, and its performance is assessed using a precision–recall curve at a confidence threshold of 0.5, achieving a mean average precision (mAP) of 0.873, indicating robust overall performance. The weight estimation procedure combines computer vision techniques to measure food volume using both RGB and depth cameras. Subsequently, density models specific to each food type are applied to estimate the detected food weight. The estimation model’s parameters are calibrated through experiments that generate volume-to-weight conversion tables for different food items. Validation of the system was conducted using rice and chicken, yielding error margins of 5.07% and 3.75%, respectively, demonstrating the feasibility and accuracy of the proposed method.https://www.mdpi.com/1424-8220/24/23/7660food weight estimationdeep learningcomputer visionartificial intelligence |
| spellingShingle | Bryan Gonzalez Gonzalo Garcia Sergio A. Velastin Hamid GholamHosseini Lino Tejeda Gonzalo Farias Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms Sensors food weight estimation deep learning computer vision artificial intelligence |
| title | Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms |
| title_full | Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms |
| title_fullStr | Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms |
| title_full_unstemmed | Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms |
| title_short | Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms |
| title_sort | automated food weight and content estimation using computer vision and ai algorithms |
| topic | food weight estimation deep learning computer vision artificial intelligence |
| url | https://www.mdpi.com/1424-8220/24/23/7660 |
| work_keys_str_mv | AT bryangonzalez automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms AT gonzalogarcia automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms AT sergioavelastin automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms AT hamidgholamhosseini automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms AT linotejeda automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms AT gonzalofarias automatedfoodweightandcontentestimationusingcomputervisionandaialgorithms |