Renbiao Wu

Papers

Total Citations

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About

Renbiao Wu is a leading figure in radar signal processing and nondestructive testing, with a particular focus on ground-penetrating radar (GPR) applications for infrastructure safety. His most cited work, "GPR-RCNN: An Algorithm of Subsurface Defect Detection for Airport Runway Based on GPR" (2021, 80 citations), exemplifies his pioneering contributions to automated inspection. Wu developed a deep learning framework that integrates GPR with a region-based convolutional neural network, enabling robots to autonomously detect subsurface defects like voids and cracks in airport runways. This innovation significantly enhances the speed and reliability of structural health monitoring, reducing reliance on manual interpretation. Beyond this, his research spans array signal processing, synthetic aperture radar (SAR), and electromagnetic inverse scattering, with applications in aviation safety and civil engineering. Wu’s work has been widely cited for its practical impact, bridging advanced algorithms with real-world infrastructure challenges. His achievements include numerous patents and collaborations with industry partners, solidifying his reputation as a transformative researcher in radar-based nondestructive evaluation. For students and researchers, Wu’s career demonstrates how signal processing and machine learning can solve critical safety problems in transportation systems.