Food Image Detection System and Calorie Content Estimation Using Yolo to Control Calorie Intake in the Body

Abstract

Excess calories in the body can cause obesity and several degenerative diseases, such as diabetes mellitus, heart disease, stroke, hypertension, and others. This system helps people maintain a balanced calorie content that enters the body. The research designs this system using the YOLO algorithm model to detect the type of food which is then developed using the Python programming language to estimate the calories of the detected food. YOLO uses the principle of feature extraction in images that are processed through filters as arrays to perform detection. This system calculates the food calories estimation by multiplying the calories for each food by the amount according to the type of food detected. The calorie value of the food provided is based on the number of calories for each portion of food taken from FatSecret Indonesia. The result is that food detection performance is quite good with average precision, recall, and F1-score values of 0.94, 0.90, and 0.91 respectively, when testing the model. However, when tested on Hugging Face, the performance decreased with the average values of precision, recall, and F1-score respectively, namely 0.84, 0.32, and 0.41. This decrease in performance is because of poor CPU usage and a decrease in image quality when uploaded to the Hugging Face.