Zepeng Shen

Papers

Total Citations

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About

Zepeng Shen is a researcher advancing the intersection of computer vision and industrial automation, with a primary focus on visual image recognition technologies for mechanical part handling. His most-cited work, "Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology" (2023), has garnered 15 citations, addressing a critical challenge in manufacturing: ensuring stable machine operation through precise part cleaning and inspection. Shen’s contributions lie in developing vision-based systems that enable robots to accurately grasp and assess mechanical components, directly impacting production efficiency and safety. By integrating image recognition with robotic manipulation, his research offers practical solutions for quality control in industrial settings, reducing downtime and human error. This work is particularly notable for its application in real-world production lines, where reliable part handling is essential. Shen’s achievements underscore his role in bridging theoretical computer vision with tangible industrial outcomes, making his research valuable for engineers and researchers seeking to automate inspection processes. His focus on robust, real-time recognition systems positions him as a contributor to the growing field of smart manufacturing and Industry 4.0.