Biaoxiong Xie

Beijing University of Civil Engineering and Architecture

Papers

Total Citations

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About

Biaoxiong Xie is a researcher advancing the intersection of deep learning and industrial automation, with a primary focus on lightweight object detection algorithms for manufacturing environments. His most notable contribution is the development of LPO-YOLOv5s, a pioneering lightweight object detection algorithm specifically designed for pouring robots in metal casting processes. This work addresses a critical industry challenge: traditional object detection methods suffer from low accuracy, while deep learning models demand excessive computational resources, hindering real-world deployment. By optimizing the YOLOv5 architecture, Xie created a solution that balances detection precision with minimal memory footprint, enabling practical implementation on resource-constrained robotic systems. His research, published in 2023, has already garnered 8 citations, signaling growing recognition within the robotics and manufacturing communities. Xie’s work is particularly significant for its direct application to the hazardous and precision-demanding task of molten metal pouring, where accurate object detection is essential for safety and quality control. His contributions exemplify the vital trend toward efficient, deployable AI in industrial settings, making him a promising voice in the field of intelligent manufacturing and computer vision for robotics.