<p>Small object detection (SOD) is a key problem in the field of computer vision. Although many existing methods have partially addressed this problem, challenges remain in optimizing SOD and balancing detection accuracy and efficiency. To address these issues, we propose a novel real-time object detector called multi-scale feature reconstruction detection transformer (MSFR-DETR). Specifically, we propose an efficient MSFR module, which is mainly used to mitigate the problem of small object feature loss in deep networks, aiming to improve the model’s perception of multi-scale features, while enhancing small object representations. Subsequently, we introduce the selective boundary aggregation (SBA) module to redesign the Neck network for multi-scale fusion, which utilizes a bidirectional fusion mechanism to solve the problem of mismatch between the spatial information of shallow features and the semantic information of deeper features. Finally, we propose the high-resolution feature fusion mechanism (HRFM) to significantly improve the model’s ability to perceive small objects without notably increasing the computational cost. Extensive experiments on three major small object benchmarks, including VisDrone, AI-TOD, and DOTA, show that MSFR-DETR achieves a good balance of accuracy and efficiency, significantly outperforming other state-of-the-art real-time detectors in small object detection.</p>