Ruikang Xu

About

Ruikang Xu is a rising researcher in computer vision, with a primary focus on low-level vision, object detection, and high dynamic range (HDR) imaging. His most notable contribution to date is the pioneering work "Toward RAW Object Detection: A New Benchmark and A New Model" (2023), which has already garnered 28 citations. This paper addresses a critical gap in real-world vision systems—such as those used in robotics and autonomous driving—by enabling object detection directly on RAW sensor data. Unlike traditional methods that rely on processed RGB images, Xu’s approach preserves the full dynamic range of the scene, allowing algorithms to perform robustly under challenging lighting conditions like strong glare or deep shadows. By introducing a new benchmark and a tailored model, he has laid the groundwork for more reliable perception systems in uncontrolled environments. Xu’s work bridges the gap between sensor physics and high-level vision tasks, demonstrating that early-stage data can significantly improve detection accuracy. As his research gains traction, he is establishing himself as a key voice in advancing practical, hardware-aware computer vision for safety-critical applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Toward RAW Object Detection: A New Benchmark and A New Model

   28 citations · 2023

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