Yongqiang Shi

Papers

Total Citations

H-Index

About

Yongqiang Shi is a researcher at the forefront of computational food science, specializing in the intersection of deep learning, computer vision, and 3D reconstruction. His work addresses the critical challenge of automated dietary assessment, with a focus on accurate calorie detection from food images. Shi’s most cited paper, “Calorie detection in dishes based on deep learning and 3D reconstruction” (2024, 6 citations), introduces a novel pipeline that combines convolutional neural networks for food recognition with 3D reconstruction techniques to estimate portion sizes—a key bottleneck in nutritional analysis. This approach enhances the precision of calorie estimation by accounting for food volume, moving beyond traditional 2D image-based methods. Though early in its citation impact, the work signals a growing interest in integrating geometric reasoning with AI for health informatics. Shi’s contributions are particularly relevant for applications in personalized nutrition, mobile health, and dietary monitoring, offering a scalable solution to combat obesity and chronic diseases. His research bridges engineering and public health, demonstrating how deep learning can transform everyday meal tracking into a reliable, data-driven tool.