FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks
This work addresses the problem of automated food tracking and nutrition estimation for users, but it is incremental as it adapts existing methods to a mobile context.
The authors tackled real-time multi-object food detection from images by developing a mobile application that uses a deep convolutional neural network integrated with YOLO, achieving nearly 80% mean average precision for recognition and localization, and providing nutrition analysis.
We present a mobile application made to recognize food items of multi-object meal from a single image in real-time, and then return the nutrition facts with components and approximate amounts. Our work is organized in two parts. First, we build a deep convolutional neural network merging with YOLO, a state-of-the-art detection strategy, to achieve simultaneous multi-object recognition and localization with nearly 80% mean average precision. Second, we adapt our model into a mobile application with extending function for nutrition analysis. After inferring and decoding the model output in the app side, we present detection results that include bounding box position and class label in either real-time or local mode. Our model is well-suited for mobile devices with negligible inference time and small memory requirements with a deep learning algorithm.