Real-time Food-Object Detection and Localization for Indian Cuisines using Deep Neural Networks

Ramesh, Abhinaav; Sivakumar, Aswath; Ángel, San

doi:10.1109/icmlant50963.2020.9355987

Cited by 9 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to justify the robustness of our proposed detection model, we analyzed the performance by using the open dataset. Since we considered the local Taiwanese cuisine for detection, local Indian cuisine [47] was considered to implement and compare our model. We implemented our food recognition model on the single-dish images obtained from the Kaggle dataset [48].…”

Section: Results On Different Datasetmentioning

confidence: 99%

Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

et al. 2022

View full text Add to dashboard Cite

Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks.

show abstract

Section: Results On Different Datasetmentioning

confidence: 99%

Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Although some work is done in image recognition in Indian food context [11], [12], almost very little or no work is done in localising or detecting multiple Indian food items in an image. In [13], the authors performed object detection using Single Shot Detector(SSD) and Inceptionv2 on Indian food dataset for 60 classes using 4200 images (only 70 images per class). However, even after using image augmentation, their images per class is relatively low which will tend to overfitting and many of the food classes in their dataset were not traditional Indian dishes (e.g.…”

Section: Related Workmentioning

confidence: 99%

Object Detection in Indian Food Platters using Transfer Learning with YOLOv4

Pandey¹,

Parmar²,

Toshniwal³

et al. 2022

Preprint

View full text Add to dashboard Cite

Object detection is a well-known problem in computer vision. Despite this, its usage and pervasiveness in the traditional Indian food dishes has been limited. Particularly, recognizing Indian food dishes present in a single photo is challenging due to three reasons: 1. Lack of annotated Indian food datasets 2. Non-distinct boundaries between the dishes 3. High intra-class variation. We solve these issues by providing a comprehensively labelled Indian food dataset-IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model. Our model is able to achieve an overall mAP score of 91.8% and f1-score of 0.90 for our 10 class dataset. We also provide an extension of our 10 class dataset-IndianFood20, which contains 10 more traditional Indian food classes.

show abstract

Dish Detection in Indian Food Platters: A Computational Framework for Diet Management

Goel,

Dargar,

Ghatak

et al. 2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Real-time Food-Object Detection and Localization for Indian Cuisines using Deep Neural Networks

Cited by 9 publications

References 12 publications

Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

Object Detection in Indian Food Platters using Transfer Learning with YOLOv4

Dish Detection in Indian Food Platters: A Computational Framework for Diet Management

Contact Info

Product

Resources

About