Papers
2
Total Citations
25
H-Index
2
About
Qirui Yang is a researcher whose work bridges computer vision and advanced manufacturing, with a focus on enhancing robotic perception and precision. His key research areas include semantic segmentation for scene understanding and sustainable machining processes for industrial robotics. Yang’s major contribution in computer vision is the development of a multilevel feature fusion dilated convolutional network, which significantly improves the accuracy and speed of semantic segmentation tasks, directly advancing robot scene perception. This work has garnered 17 citations, reflecting its growing influence in the field. In manufacturing, Yang introduced a novel fusion modeling method for predicting burr formation in weakly rigid drilling processes, such as those performed by industrial robots. This sustainable processing mode addresses critical precision challenges in aerospace and military applications, earning 8 citations for its practical impact. Yang’s interdisciplinary approach—combining deep learning with mechanical engineering—demonstrates a unique ability to solve real-world problems, from enhancing autonomous systems to improving part quality in high-stakes industries. His work is notable for its dual focus on algorithmic innovation and sustainable manufacturing, making him a promising figure in both computer vision and production engineering.
Research Focus
Key Achievements
Top Papers
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