Bite-Sized Innovations: An In-Depth Review of Deep Learning Approaches to Food Recognition
Year: 2024
Type: article
Abstract: This research centers on creating a cutting-edge application designed to automatically detect and localize food objects in real-time settings. Whether used as a standalone tool or integrated into a connected application framework, this solution aims to offer flexibility and user-friendliness. To ensure accurate food detection, we've trained a variety of advanced algorithms, including Single Shot Detection (SSD), Faster R-CNN, YOLO, EfficientDet, RetinaNet, and custom architectures. Our training utilized a rich dataset gathered from various online sources, providing a diverse array of food representations. We've carefully matched these algorithms with a specialized food detection model, using multiple convolutional network architectures to maximize performance. In this paper, we share several deep learning techniques for food detection, showcasing their effectiveness and potential applications across different fields.
Authors: R. Sanjana, J. Umesh chandra, M. Nikesh, M. Bharathi
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6949614953
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