Papers
3
Total Citations
29
H-Index
3
About
Yangke Li is a researcher at the forefront of applied deep learning, with a focus on environmental sensing and surgical robotics. Their work bridges computer vision and real-world problem-solving, particularly in waste management and autonomous medical systems. Li’s most cited paper, “Multi-modal deep learning networks for RGB-D pavement waste detection and recognition” (2024, 20 citations), introduces a novel framework that fuses color and depth data to improve the accuracy of detecting and classifying debris on roadways—a critical step toward smarter, cleaner urban infrastructure. In the medical domain, Li contributed to the “SurgRIPE challenge: Benchmark of surgical robot instrument pose estimation” (2025, 5 citations), which addresses the vital need for markerless, vision-based tracking in robotic surgery. This work lays the groundwork for autonomous surgical task execution by enabling precise instrument localization. Additionally, Li’s “Lightweight deep learning model for underwater waste segmentation based on sonar images” (2024, 4 citations) demonstrates a commitment to scalable, efficient AI for environmental monitoring, optimizing models for deployment in resource-constrained settings. With a growing citation record and contributions spanning both terrestrial and aquatic domains, Yangke Li is establishing a reputation for impactful, application-driven research that tackles pressing challenges in sustainability and healthcare.
Research Focus
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Top Papers
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