Jianqiang Xia

Wuhu Hit Robot Technology Research Institute

Papers

Total Citations

H-Index

About

Jianqiang Xia is a leading researcher in intelligent manufacturing and robotics, with a specialized focus on object detection algorithms for industrial automation, particularly in the challenging environment of metal casting. His work addresses a critical bottleneck in the foundry industry: the need for accurate, real-time visual perception to enable robots to autonomously perform complex tasks like pouring molten metal. Xia’s major contributions include the development of two pioneering algorithms: LPO-YOLOv5s and CP-RDM. LPO-YOLOv5s, a lightweight deep learning model, tackles the dual challenge of high accuracy and low computational resource requirements, making it suitable for deployment on edge devices in harsh factory settings. His subsequent work, CP-RDM, further refines detection capabilities for intricate pouring holes in cluttered workshops. While his most-cited papers are recent (2023–2024), they have already garnered 10 citations, signaling growing impact in the field. Xia’s research directly advances the intelligence of the casting process, promising safer, more efficient, and more automated production lines. His work is essential reading for engineers and researchers developing vision-guided robotic systems for heavy industry.