Towards Efficient, Accurate, and Scalable Food Dish Recognition

Abstract

The ability to accurately and efficiently recognize food dishes has become an important area of research, driven by applications in automated food logging, restaurant services, and dietary monitoring. Current food recognition systems, while achieving impressive results, often face challenges related to speed, scalability, and practical deployment in real-world scenarios. This paper explores recent advancements in food dish recognition, focusing on developing models that balance computational efficiency with high accuracy. We examine novel approaches, such as the integration of deep learning techniques, image processing methods, and transfer learning, to improve performance while ensuring the system is practical for everyday use. Additionally, we discuss challenges related to diverse cuisines, variable plating styles, and environmental factors, and propose strategies to address these issues. By investigating scalable and adaptable solutions, this work aims to provide a framework for building fast, reliable, and widely applicable food recognition systems, making them more viable for real-world applications such as mobile apps, restaurant ordering, and health monitoring.

Keywords: Food Dish Recognition, Deep Learning, Image Classification, Convolutional Neural Networks (CNN), Scalable Food Recognition, Food Image Dataset, Food Identification Algorithms, Accuracy in Food Recognition, AI in Food Applications, Real-time Food Recognition