Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting

Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks.

Automated summary

Artificial intelligence (AI) has significant applications in preventive healthcare, particularly in dietary intake reporting for assessing nutrient content. Traditional dietary assessment methods are cumbersome and time-consuming, but computer vision technology provides potential for improvement. This research proposes an AI-based multiple-dish food recognition model using the EfficientDet deep learning model, specifically targeting local Taiwanese cuisine. The results show high accuracy and the potential for enhancing dish reporting.