Papers
1
Total Citations
13
H-Index
1
About
Yeonjun Bang is a rising researcher at the forefront of autonomous systems and computer vision, with a specialized focus on electric vehicle (EV) charging automation. His most-cited work, "Image-to-Image Translation-Based Data Augmentation for Robust EV Charging Inlet Detection" (2022, 13 citations), tackles a critical bottleneck in the deployment of autonomous EV charging robots: the reliable detection of charging inlets under diverse real-world conditions. Bang’s major contribution lies in leveraging generative adversarial networks (GANs) for data augmentation, creating synthetic yet realistic training images that dramatically improve detection robustness against variations in lighting, weather, and inlet orientation. This innovative approach directly addresses a key challenge in smart infrastructure, aiming to enhance user experience and optimize the utilization of charging stations and parking lots. While early in his career, Bang’s work signals a significant step toward practical, fully automated EV charging systems. His research bridges the gap between cutting-edge deep learning techniques and tangible engineering solutions, positioning him as a promising contributor to the future of intelligent transportation and sustainable energy infrastructure.
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Top Papers
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