• rajesh kumar s
• Dr. Josephine Prem Kumar

Abstract: The goal of this paper is to create an application that can be used in a standalone or connected application framework that can automatically detect and localise food objects in real-time scenes. The Single Shot Detection, Faster R-CNN, YOLO, EfficientDet, RetinaNet, and Mask R-CNN configurations were trained using a dataset that was assembled from various online sources. These algorithms were paired with the food detection model and multiple convolutional network architectures; in this case, multiple neural networks will be used. We have presented a few deep learning-based techniques for food detection in this paper.

Deep Learning, SSD, EfficientDet, YOLO, Faster R- CNN, RetinaNet, Mask R-CNN

"Real-Time Food Detection and Localization Using Deep Learning: A Comparative Study of Single Shot Detection, Faster R-CNN, YOLO, EfficientDet, RetinaNet, and Mask R-CNN.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.g195-g205, August-2024, Available :http://www.jetir.org/papers/JETIR2408721.pdf

"Real-Time Food Detection and Localization Using Deep Learning: A Comparative Study of Single Shot Detection, Faster R-CNN, YOLO, EfficientDet, RetinaNet, and Mask R-CNN.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppg195-g205, August-2024, Available at : http://www.jetir.org/papers/JETIR2408721.pdf