Paper
29 August 2024 Deep food insight: a transfer learning approach for food detection and nutrient estimation through image analysis
Jahanzaib Yaqoob, Ruirui Li, Hu Yuandong, Umair Javed, Yukun Yang, Kexin Meng, Naihao Wang, Yiran Liu
Author Affiliations +
Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 1324902 (2024) https://doi.org/10.1117/12.3044787
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China
Abstract
Dietary choices have a substantial impact on the health of an individual. This AI-driven research aims to recognize, classify, and estimate the origin and nutrition of food. The proposed system is trained using a diverse dataset containing images of food (101 Classes) in different lights and environmental conditions. In this research a transfer learning approach applied with ResNet and InceptionV3 architectures using their pre-trained weights with finetuning of hyperparameters (Learning rate, Batch size and Optimizer). As a result of this approach, the ability to learn intricate features relevant to food recognition was retained while training rapidly. The system achieves impressive accuracy: 96.6% and 96.1% respectively for food identification, nutrient, and origin estimation. The system accurately recognizes popular foods like pizza, sushi, and salads, even in low light. Furthermore, to provide reliable food information to end users, we have developed a user-friendly web application. The app allows users to upload pictures of their meals to receive nutritional and origin information, empowering them to make healthier choices. This simplifies the process of making informed dietary choices for individuals.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jahanzaib Yaqoob, Ruirui Li, Hu Yuandong, Umair Javed, Yukun Yang, Kexin Meng, Naihao Wang, and Yiran Liu "Deep food insight: a transfer learning approach for food detection and nutrient estimation through image analysis", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 1324902 (29 August 2024); https://doi.org/10.1117/12.3044787
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KEYWORDS
Education and training

Data modeling

Deep learning

Image analysis

Image classification

Image processing

Performance modeling

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