Mou Jinzhuo

Beijing Research Institute of Automation for Machinery Industry (China)

Papers

Total Citations

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About

Mou Jinzhuo is a researcher whose work bridges computer vision and industrial automation, with a focus on enhancing safety in hazardous environments. His key research areas include object detection, pointer meter recognition, and robotic inspection systems. Mou’s most notable contribution is his 2023 paper, "Research on Two-step Pointer Meter Recognition Method Based on Yolov7," which proposes a novel two-step recognition approach using the YOLOv7 architecture. This method enables explosion-proof inspection robots to autonomously read analog meters in dangerous chemical sites, significantly reducing the need for human exposure to high-risk areas. While his work is early in its citation impact, with 3 citations to date, it addresses a critical gap in industrial safety by combining deep learning with practical robotics. Mou’s research stands out for its direct applicability to real-world challenges, offering a scalable solution for automated monitoring in environments where manual inspection is perilous. His work exemplifies how computer vision can be leveraged to improve workplace safety and operational efficiency, marking him as an emerging contributor to the field of intelligent inspection systems.