Deep Learning in Food Image Recognition: A Comprehensive Review

Abstract

Food not only fulfills basic human survival needs but also significantly impacts health and culture. Research on food-related topics holds substantial theoretical and practical significance, with food image recognition being a core task in fine-grained image recognition. This field has broad applications and promising prospects in smart dining, intelligent healthcare, and smart retail. With the rapid advancement of artificial intelligence, deep learning has emerged as a key technology that enhances recognition efficiency and accuracy, enabling more practical applications. This paper comprehensively reviews the techniques and challenges of deep learning in food image recognition. First, we outline the historical development of food image recognition technologies, categorizing the primary methods into manual feature extraction-based and deep learning-based approaches. Next, we systematically organize existing food image datasets and summarize the characteristics of several representative datasets. Additionally, we analyze typical deep learning models and their performance on different datasets. Finally, we discuss the practical applications of food image recognition in calorie estimation and food safety, identify current research challenges, and propose future research directions.

Keywords

• food image recognition
• deep learning
• computer vision
• food datasets