  End-to-end transformer-based detectors (DETRs) have shown exceptional
performance in both closed-set and open-vocabulary object detection (OVD) tasks
through the integration of language modalities. However, their demanding
computational requirements have hindered their practical application in
real-time object detection (OD) scenarios. In this paper, we scrutinize the
limitations of two leading models in the OVDEval benchmark, OmDet and
Grounding-DINO, and introduce OmDet-Turbo. This novel transformer-based
real-time OVD model features an innovative Efficient Fusion Head (EFH) module
designed to alleviate the bottlenecks observed in OmDet and Grounding-DINO.
Notably, OmDet-Turbo-Base achieves a 100.2 frames per second (FPS) with
TensorRT and language cache techniques applied. Notably, in zero-shot scenarios
on COCO and LVIS datasets, OmDet-Turbo achieves performance levels nearly on
par with current state-of-the-art supervised models. Furthermore, it
establishes new state-of-the-art benchmarks on ODinW and OVDEval, boasting an
AP of 30.1 and an NMS-AP of 26.86, respectively. The practicality of
OmDet-Turbo in industrial applications is underscored by its exceptional
performance on benchmark datasets and superior inference speed, positioning it
as a compelling choice for real-time object detection tasks. Code:
\url{https://github.com/om-ai-lab/OmDet}
