Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 339-356
Abstract
This study aims to develop a system for estimating the portion size and energy of Thai food from images using deep learning techniques. The proposed system supports dietitians and health-conscious individuals by enabling automated and accurate food intake assessment. The system consists of two main components: (1) object detection using YOLOv11 to simultaneously identify food items and reference coins in an image, and (2) food weight estimation using ResNet101, with coin objects serving as physical references for real-world scaling. The estimated food weight is then used to calculate nutritional values based on a Thai food database. Experimental results demonstrate that annotating object boundaries with Smart Polygon significantly improves model accuracy and stability compared to the traditional Bounding Box method, yielding higher Precision, Recall, F1-score, and mAP. Among the tested models, ResNet101 with coin references achieved the best weight estimation performance, with a Mean Absolute Error (MAE) of 71.12 grams and Root Mean Squared Error (RMSE) of 91.56 grams. This system is suitable for real-world applications in hospitals, restaurants, and personal nutrition tracking.