International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Vision Based Food Analysis System Marshal Panigrahy1, Onkar Patil2, Pratheek Shetty3, Swati Gajbhiye4 1-3Information
Technology Department, Shah & Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India Information Technology Department, Shah & Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------4Professor,
Abstract - Part of people consume ill foods on purpose
employs RoIAlign to unify the RoI size. As a result, the accuracy rate increases by at least 10% when a RoI enters the mask branch prediction mask [1].
cause of variety of reasons, while others do it without thinking. Fatness, diabetes, and other lifelong illnesses are all linked to poor dietary attitude and dental diseases. Food picture revival & categorization could potentially replace the inaccurate personal nutritional evaluation, that relies heavily upon selfreporting.
Regarding [1], particular elements of the food picture are employed as the foundation for categorization in the image processing approach, and there is space for improvement in terms of accuracy. Color and texture are low-level particular characteristics in the deep learning-based technique. A deep neural network can learn abstract properties that can aid with food detection and recognition. As a result, this work employs Mask R-CNN to detect food pictures, a linear regression computation to estimate food weight, and a nutritional table to estimate food calories and nutrients [1].
Key Words: ML, Food, Recipe, Nutrition, Food picture Recognition, Food Nutrition Analysis.
1. INTRODUCTION Food is vital to mankind's existence as well as the world wealth. Human's awareness has lately been drawn to food well-being & excellent nutritional enactment. Numerous studies over current age concluded as artificial intelligence and cv approaches could aid in the development of technique that can detect objects impulsively food and to estimate its nutrients. Often these systems exploit mobile applications for food recognition. Part of people consume ill foods on purpose cause of variety of reasons, while others do it without thinking. Fatness, diabetes, and other lifelong illnesses are all linked to poor dietary attitude and dental diseases. Food picture revival & categorization could potentially replace the inaccurate personal nutritional evaluation, that relies heavily upon self-reporting. Clearly, the true comparison of these systems necessitates the accessibility of appropriate repositories that accurately reflect the complexities of the food identification job.
The proposed system by M. -L. Chiang [1] picture rescaling, food identification and categorization, food amount detection, and food total energy consumed & nutrition report are the 4 key phases. You give the system a food image as input then it will resize the image size. The new image is then sent for recognition using Mask R-CNN. Once the item has been detected it will then detect the approx. weight and also the calorie and nutrition value. The results of this system came out to be quite nice. They faced some challenges like with the weight prediction for which they needed the image to be taken from different angles. The different angle images help to have a correct ratio of how much food there can be. The dataset that they used had a total of 850 four hundred and twenty-eight testament pictures and four hundred and twenty-eight preparation images There were 4,118 & 1,978 food products examined and verified, individually, [1].
2. PREVIOUS WORK 2.1 Food Calorie and Nutrition Analysis System based on Mask R-CNN
As in the 16 types, the mean precision measure of food identification were nighty-nine percent. 3,568 items were identified out of 3,680 foods tested, with 5 foods misidentified & hundred and twelve foods unable to be predicted [1].
According to own-divulge of its fat individuals' provisions [1], around thirty-three percent of respondents miscalculate the quantity of food consumed. The authors in this paper tried to develop a system which will help in monitoring the calorie intake by a person and what nutritional value he is adding to his body.
2.2 Image-Based Estimation of Real Food Size for Accurate Food Calorie Estimation Here the authors [2] have tried to create a system which will automatically find the calorie estimation of a food item provided to the system. For this purpose, T. Ege [2] and his colleagues have proposed two methods based on the three studies that they have reviewed. “DepthCalorieCam” which is a food calorie estimation system exploiting iPhone stereo
Mask R-CNN [1] is an improved version of quicker R-CNN for instance segmentation. The convolutional backbone, the RPN, the RoIAlign layer, and the head make up its structure. Mask R-CNN enhances the positional deviation of the frame selection object when compared to quicker R-CNN since it
© 2022, IRJET
|
Impact Factor value: 7.529
|
ISO 9001:2008 Certified Journal
|
Page 163