In recent years, attention mechanisms have significantly enhanced the performance of object detection by focusing on key feature information. However, prevalent methods still encounter difficulties in effectively balancing local and global features. This imbalance hampers their ability to capture both fine-grained details and broader contextual information-two critical elements for achieving accurate object detection.To address these challenges, we propose a novel attention mechanism, termed Local-Global Attention, which is designed to better integrate both local and global contextual features. Specifically, our approach combines multi-scale convolutions with positional encoding, enabling the model to focus on local details while concurrently considering the broader global context. Additionally, we introduce a learnable parameters, which allow the model to dynamically adjust the relative importance of local and global attention, depending on the specific requirements of the task, thereby optimizing feature representations across multiple scales.We have thoroughly evaluated the Local-Global Attention mechanism on several widely used object detection and classification datasets. Our experimental results demonstrate that this approach significantly enhances the detection of objects at various scales, with particularly strong performance on multi-class and small object detection tasks. In comparison to existing attention mechanisms, Local-Global Attention consistently outperforms them across several key metrics, all while maintaining computational efficiency.

本研究解决了现有注意力机制在对象检测中局部与全局特征平衡不足的问题，影响了细节和背景信息的捕捉。提出的新型局部-全局注意力机制结合了多尺度卷积与位置编码，不仅关注局部细节，也兼顾全局上下文，通过可学习的参数动态调整局部与全局注意力的重要性，显著提升了不同尺度对象的检测能力，尤其在多类和小物体检测任务中表现优异。

局部-全局注意力：一种自适应机制用于多尺度特征融合