Zijun Su

Papers

Total Citations

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About

Zijun Su is a researcher focused on advancing automated inspection and quality control in aerospace and industrial applications, with a particular emphasis on computer vision and deep learning. Their most cited work, "Detection and State Classification of Bolts Based on Faster R-CNN" (2022, 2 citations), addresses a critical challenge in aircraft maintenance: the need for rapid, accurate detection and classification of bolts—key connectors whose hardness and state directly impact safety. By applying the Faster R-CNN framework, Su developed a method to automate what is traditionally a manual, time-intensive inspection process, significantly improving efficiency as training frequencies for aircraft increase. This contribution highlights Su’s expertise in integrating object detection algorithms with real-world industrial demands, offering a scalable solution for quality assurance. While still early in their career, Su’s work demonstrates a clear commitment to bridging cutting-edge AI techniques with practical engineering challenges, laying the groundwork for future innovations in automated maintenance systems. Their research is particularly valuable for students and engineers exploring the intersection of deep learning and aerospace safety.