Authors - Shilpa Dhopte, Lalit Damahe Abstract - The food portion estimation is a critical component of automated dietary assessment systems, enabling better monitoring of nutritional intake and supporting healthcare, weight management, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing, deep learning–based segmentation, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy, with a segmentation IoU of 87.6%, precision of 90.3%, recall of 88.9%, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%, resulting in an overall portion estimation accuracy of 89.1%, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
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