Bangladeshi Street Food Calorie Estimation Using Improved YOLOv8 and Regression Model
This addresses calorie tracking for people in Bangladesh, focusing on a specific cuisine not well-covered in prior work, but it is incremental as it adapts existing methods to new data.
The paper tackled automated calorie estimation for Bangladeshi street food by developing a modified YOLOv8 model and regression system, achieving a 6.94 MAE, 11.03 RMSE, and 96.0% R^2 score.
As obesity rates continue to increase, automated calorie tracking has become a vital tool for people seeking to maintain a healthy lifestyle or adhere to a diet plan. Although numerous research efforts have addressed this issue, existing approaches often face key limitations, such as providing only constant caloric output, struggling with multiple food recognition challenges, challenges in image scaling and normalization, and a predominant focus on Western cuisines. In this paper, we propose a tailored solution that specifically targets Bangladeshi street food. We first construct a diverse dataset of popular street foods found across Bangladesh. Then, we develop a refined calorie estimation system by modifying the state-of-the-art vision model YOLOv8. Our modified model achieves superior classification and segmentation results, with only a slight increase in computational complexity compared to the base variant. Coupled with a machine learning regression model, our system achieves an impressive 6.94 mean absolute error (MAE), 11.03 root mean squared error (RMSE), and a 96.0% R^2 score in calorie estimation, making it both highly effective and accurate for real-world food calorie calculations.