Chentong Li

Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Sciences

Papers

Total Citations

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About

Chentong Li is a researcher specializing in computer vision, human-computer interaction, and deep learning-based object detection, with a particular focus on gesture recognition and intelligent waste classification systems. Their work addresses critical challenges in real-world applications, including the accuracy of gesture detection under occlusion and similarity constraints, as well as the automation of municipal solid waste sorting to reduce environmental pollution and occupational hazards. Li’s most-cited paper, “A Gesture Recognition Method Based on Yolov4 Network” (2021, 2 citations), proposes an enhanced detection framework for collaborative robotics and smart home control, improving robustness in complex interaction scenarios. A subsequent study, “An application case of object detection model based on Yolov3-SPP model pruning” (2022, 2 citations), demonstrates a practical, resource-efficient approach to classifying household waste, addressing both economic and health risks associated with manual sorting. Although early in their career, Li’s contributions highlight a commitment to deploying efficient, scalable vision models for socially impactful applications. Their work bridges the gap between cutting-edge deep learning architectures and pressing environmental and ergonomic challenges, offering promising directions for future research in sustainable automation and intuitive human-machine interfaces.