Object detection has greatly improved over the past decade, thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected on 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 an extensive benchmark specifically designed for Reflected Object Detection. Our Reflected Object Detection (ROD) dataset 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. The ROD dataset encompasses 10 categories and 6 reflective surfaces, including 23,520 images of real and reflected objects on different backgrounds, complete with standard bounding-box annotations and the classification of objects as real or reflected. In addition, we present baseline results by adapting five state-of-the-art object detection models to address this challenging task. The experimental results underscore the limitations of existing methods when applied to reflected object detection, highlighting the need for specialized approaches. By releasing the ROD dataset, we aim to support and advance future research on detecting reflected objects. The dataset and code are available at: https://github.com/jirouvan/ROD .