Simulators in robotics research are widely used and are the cornerstones of many advances in the field. As time goes on, there are now more and more options available to roboticists than ever before. However, navigating through the choices in search of the right simulator is often non-trivial. There is a lack of quantitative studies to justify the usage of different simulators for different applications, especially regarding the quality of simulated computer vision solutions. A common vision solution with inaccuracies in simulators is object detection. We conduct thorough quantitative experiments and provide in-depth analysis of comparability of object detection performance in the commonly used simulator Gazebo and the newly introduced NVIDIA Isaac Sim to real life solutions using the Toyota Human Support Robot (HSR) as the platform. Our results indicate that Gazebo grossly overestimates in terms of prediction accuracy while NVIDIA Isaac Sim matches much more closely with reality. Additionally, we found that performance in Gazebo can be unstably variant at times. Furthermore, both simulators share highly correlated low-end to high-end performance distribution with reality. We can recommend NVIDIA Isaac Sim for robotic research and developments where computer vision is integral.