Ye Yuting

Papers

Total Citations

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About

Ye Yuting is a rising researcher in computer vision and deep learning, with a primary focus on object detection and recognition in complex indoor environments. Their most cited work, "Indoor object recognition based on YOLOv5 with EIOU loss function" (2023), addresses a critical challenge in large-scale classification tasks: the trade-off between training efficiency and model accuracy. By integrating the Efficient Intersection over Union (EIOU) loss function into the YOLOv5 framework, Ye demonstrated how a refined loss definition can accelerate convergence and improve detection precision with fewer training epochs. This contribution is particularly significant for real-world applications like robotics and smart spaces, where reliable indoor object recognition is essential. Although early in their career—with 3 citations to date—Ye’s work highlights a deep understanding of loss function optimization, a cornerstone of modern deep learning. Their research offers a practical pathway for enhancing model performance without excessive computational cost, making it valuable for students and practitioners seeking efficient solutions in object detection. Ye Yuting is a promising voice in the ongoing effort to bridge algorithmic theory and applied vision systems.