Papers
1
Total Citations
2
H-Index
1
About
YiKang is a researcher focused on advancing computer vision and autonomous robotics, with a particular emphasis on real-time object detection and tracking for ground mobile robots. Their major contribution lies in enhancing the efficiency and speed of deep learning-based detection systems, specifically by addressing critical bottlenecks in the Single Shot Multibox Detector (SSD) network. In their most-cited work, "Target Detection and Tracking of Ground Mobile Robot Based on Improved Single Shot Multibox Detector Network" (2021), YiKang proposed a fast data set labeling algorithm to overcome the slow labeling speeds of traditional tools like labellmg, while also optimizing the SSD network to improve classification and detection runtime. This work, with 2 citations, demonstrates practical impact in making deep learning models more deployable for real-world robotic applications. YiKang’s research bridges the gap between algorithmic efficiency and autonomous system performance, offering valuable solutions for engineers and researchers working on mobile robot perception. Their contributions are particularly notable for addressing both data preparation and model inference challenges, highlighting a systems-level approach to advancing field robotics.
Research Focus
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Top Papers
- 1