  Object detection has greatly improved over the past decade thanks to advances
in deep learning and large-scale datasets. However, detecting objects reflected
in surfaces remains an underexplored area. Reflective surfaces are ubiquitous
in daily life, appearing in homes, offices, public spaces, and natural
environments. Accurate detection and interpretation of reflected objects are
essential for various applications. This paper addresses this gap by
introducing a extensive benchmark specifically designed for Reflected Object
Detection. Our Reflected Object Detection Dataset (RODD) features a diverse
collection of images showcasing reflected objects in various contexts,
providing standard annotations for both real and reflected objects. This
distinguishes it from traditional object detection benchmarks. RODD encompasses
10 categories and includes 21,059 images of real and reflected objects across
different backgrounds, complete with standard bounding box annotations and the
classification of objects as real or reflected. Additionally, we present
baseline results by adapting five state-of-the-art object detection models to
address this challenging task. Experimental results underscore the limitations
of existing methods when applied to reflected object detection, highlighting
the need for specialized approaches. By releasing RODD, we aim to support and
advance future research on detecting reflected objects. Dataset and code are
available at: https: //github.com/Tqybu-hans/RODD.
