Beyond Limitations: Omni-DETR for Comprehensive Object Detection in Real-Time Applications

In the field of autonomous driving, the performance of object detectors is crucial, as they must not only provide accurate and reliable environmental perception but also meet the demands for high inference speed and lightweight model requirements. Although the RT-DETR model, based on the Transformer architecture, has demonstrated high speed and accuracy in real-time end-to-end object detection, its performance in detecting small objects is suboptimal. To address this issue, we introduce Omni-DETR, an advanced model aimed at enhancing detection accuracy for small objects without compromising efficiency. Omni-DETR incorporates FasterIRANet as its backbone for feature extraction, significantly reducing redundant computations and memory access, thereby achieving high inference speed while enhancing accuracy. In the encoder, we propose the Dimensional Feature Integrator DFI , which strengthens the model's capability to capture multi-scale features. Additionally, we design a novel bounding box regression loss function, InnerMPDIoU. Experimental results on the TT100K dataset demonstrate that Omni-DETR achieves an AP of 61.2 and a processing speed of 42.8 FPS on a 3090 GPU, while attaining 53.7 AP on the COCO dataset. Compared to several existing models, Omni-DETR proves its superiority in comprehensive performance.