Estimation method and research of fruit glycemic load index based on the fusion SE module Faster R-CNN

Abstract

In recent years, with the close connection between the field of artificial intelligence and life, people's quality of life has been improved, and food choices have increased. However, how to understand one's own dietary structure and sugar intake and obtain the correct glycemic load index reference has become a more concerned issue for diabetic patients. This paper studies how to measure the dietary intake of diabetic patients. Our purpose is to identify fruits with high sweetness and low glycemic load value, to help diabetic patients give correct and reasonable reference to their daily diet. For better research, we constructed a fruit dataset for studying the daily diet of diabetic patients. At the same time, we improve the Faster R-CNN network for fruit recognition, introduce an attention mechanism module in the feature extraction stage, adjust the anchor aspect ratio of the Region Proposal Network (RPN), and perform a fusion update operation in the fully connected layer. Experiments show that, compared with existing methods, our method im-proves the precision and average recall rate by 2.47% and 3.06%, respectively. Then we designed a volume estimation method, and then obtained the glycemic load index of the fruit, and finally gave an eating opinion based on this value.