Qian Wan
Papers
1
Total Citations
11
H-Index
1
About
Qian Wan is a researcher advancing intelligent manufacturing and industrial automation, with a primary focus on computer vision and deep learning for production line monitoring. Their most-cited work, "Equipment Identification and Localization Method Based on Improved YOLOv5s Model for Production Line" (2022, 11 citations), addresses a critical challenge in the Industry 5.0 era: the low recognition accuracy and poor localization precision of equipment in intelligent video surveillance. By enhancing the YOLOv5s model, Wan developed a method that significantly improves both the detection and spatial positioning of machinery on factory floors, enabling more reliable, real-time monitoring without human intervention. This contribution bridges the gap between AI-driven image processing and practical industrial deployment, offering a scalable solution for smart factories. Wan’s research sits at the intersection of artificial intelligence, image processing, and production line optimization, demonstrating how cutting-edge object detection models can be tailored for real-world manufacturing environments. Their work is particularly notable for its focus on operational efficiency and safety, providing a foundation for future innovations in autonomous industrial inspection and predictive maintenance.
Research Focus
Key Achievements
Top Papers
- 1