Enhancing object detection efficiency with transformers through multi-level feature integration

This paper presents a novel approach to enhancing object detection efficiency by integrating multi-level features within a transformer architecture. Traditional object detection methods often rely on single-level feature representations, which may limit their ability to accurately detect objects of varying sizes and complexities. By leveraging multi-level feature integration within the transformer framework, our method captures a richer set of spatial and semantic information, leading to more precise and robust object detection.The powerful attention mechanisms of transformers are utilized to effectively combine these features, improving detection accuracy and localization. The proposed approach is evaluated on the PASCAL VOC benchmark dataset, demonstrating superior performance over conventional single-level feature-based methods. Experimental results show that our model achieves an mAP@0.5 of 87% on PASCAL VOC, outperforming recent state-of-the-art methods while maintaining computationally efficient. These findings highlight the potential of multi-level feature integration within transformers in advancing the field of object detection.