In this paper, we address the problem of small object detection (SOD) by introducing our novel approach - Dynamically Multiplexed Expanded Features Set (DM-EFS) form. Detecting small objects is challenging as they usually suffer from inadequate feature representation. Hence, to address this, we propose the Expanded Features Set (EFS) form - a simple yet effective idea to improve the feature representation of small objects by utilizing the untapped higher resolution features from the shallower layers of the backbone module. We observe that the EFS form improves the SOD performance. However, due to processing of additional features, it has a higher computational cost which reduces inference efficiency. Hence, to address this, we propose Dynamic Feature Multiplexing (DFM) - a novel design that optimizes the usage of the EFS form during inference by dynamically multiplexing it to create our aforementioned DM-EFS form. Since our DM-EFS form is a multiplexed (or subsampled) optimal version of the EFS form, it improves the SOD performance like the EFS form but with a lower computational cost. Extensive experiments confirm the efficacy of our DM-EFS approach. Integrated with YOLOv7 base model, our DM-EFS achieves state-of-the art results on diverse SOD datasets outperforming the base model and SOD baselines, with on-par or even better inference efficiency.