Food Detection

Food detection research focuses on automatically identifying and quantifying food items in images and videos, primarily to improve dietary assessment and management. Current efforts leverage deep learning, employing architectures like YOLO, Faster R-CNN, and MobileNet, often combined with novel approaches such as zero-shot learning and multimodal models (e.g., incorporating text and image data). This technology has significant implications for healthcare (nutrition counseling, disease prevention), personal wellness (dietary tracking, personalized recommendations), and smart home applications (automated grocery management).