TL;DR
Convolution Transformer Integration Network (CTNet) is proposed, a hybrid architecture that couples a residual CNN branch for local detail mining with a Transformer branch for global dependency modeling and achieves effectiveness in food recognition.
Maintaining a healthy diet requires accurate knowledge of the food consumed and its nutritional value. However, manually identifying food items and estimating their calorie content can be difficult and time-consuming. This project, "AI-Driven Smart Food Perception and Nutrition Assessment," presents an intelligent system that automatically recognizes food items from images and provides an estimate of their calorie content using deep learning techniques. The proposed framework employs a Convolutional Neural Network (CNN) to learn visual features from food images and classify different food categories with high accuracy. Before model training, the dataset undergoes preprocessing steps such as image resizing, normalization, and shuffling to improve learning performance. After recognizing the food item, the system retrieves its nutritional information from a calorie database and calculates the estimated calorie intake. It also keeps track of the total calories consumed and the remaining daily calorie allowance, helping users monitor their eating habits. The developed application provides a simple graphical interface for food image upload, model prediction, and nutritional assessment. Overall, the proposed system offers an efficient and practical solution for supporting healthier food choices and improving daily dietary management.
This paper presents a real-time food recognition and calorie estimation system that leverages the YOLOv8 nano object-detection architecture coupled with a curated nutritional knowledge base and outperforms prior approaches based on Faster R-CNN and SSD MobileNet on both speed and accuracy metrics.
Dietary monitoring and health care will both require accurate food recognition and nutritional analysis. The current methods mainly involve convolutional networks that analyse images and word embedding that process ingredients as distinct processes with dish identification, portion size, and nutrition labelling as independent. This paper presents a multimodal transformer-based framework called as ViT-FoodNA that integrates these tasks into one end-to-end framework. It suggests using Vision Transformer (ViT), which is used to encode the visual attributes of food images, and Transformer based encoder to learn semantic relationships between inputs of ingredients. A cross-modal fusion transformer consists of visual and textual embedding to facilitate contextual interaction between the images of foods and their ingredients. Based on the result of the fused representation, the model collectively predicts the identity of the dish along with portion size and comprehensive nutritional breakdown using a shared transformer decoding strategy. In contrast to the previous convolutional neural network (CNN)-based embedding structures, the suggested architecture uses the self-attention mechanism to learn fine-grained intermodal dependencies, which enhances its resistance to visual ambiguity and missing ingredients in a list. Experiments indicate that ViT-FoodNA produces up to 12% higher Precision, Recall, F1-score, and 9.75% and 5% greater Top-1 and Top-5 accuracy and up to 32.7% and 30.4% lower MAE and RMSE than existing models. The reduction of PMAE is 6.5%, and the increase of mAP is 9.8%, which proves the great benefit of transformer-based multimodal fusion in the full analysis of diet.
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