Abstract <p>This paper focuses on advancing the field of small object detection within complex visual environments, leveraging the latest in deep learning technologies. We introduce a novel approach characterized by a dual-stream self-attention mechanism integrated within a multihead framework, and further refine detection accuracy through an innovative output reweighting technique. The core of our methodology, termed ADSAR (advanced dual-stream attention and reweighting), is designed to tackle the challenges posed by small objects that often overlap multiple tokens in feature maps—a common issue in conventional detection models. By dynamically adjusting the scale of attention across different heads, ADSAR allows for detailed feature capture at multiple granularities, significantly enhancing the model’s ability to detect and characterize small objects. The addition of a softmax-based reweighting function selectively emphasizes features crucial for object recognition, thereby suppressing irrelevant information and reducing noise. Our proposed model not only outperforms existing state-of-the-art solutions in accuracy but also demonstrates superior efficiency in processing and scalability. These advancements contribute to the theoretical understanding of attention mechanisms in deep neural networks and offer practical improvements in real-world applications where small object detection is critical.</p>