Xiaoming Guo

Papers

Total Citations

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About

Xiaoming Guo is a researcher in robotics and computer vision, with a primary focus on advancing deep learning techniques for industrial automation. His most cited work, "Research on robot target recognition based on deep learning" (2021), tackles a critical challenge in manufacturing: enabling robots to accurately identify and handle workpieces that are randomly placed or stacked together. By improving the SSD (Single Shot MultiBox Detector) algorithm, Guo’s research enhances machine vision systems’ ability to recognize objects in cluttered, unstructured environments—a key step toward more flexible and intelligent robotic systems. With 7 citations, this paper has already begun influencing subsequent studies in industrial robotics and deep learning-based perception. Guo’s contributions are particularly valuable for bridging the gap between traditional, rigid machine vision and adaptive, learning-based approaches. His work supports the development of robots that can operate autonomously in dynamic settings, reducing the need for manual programming and increasing efficiency in manufacturing lines. For students and researchers exploring the intersection of deep learning and robotics, Guo’s research offers a practical, application-driven perspective on solving real-world industrial problems.