In the field of detection, there is a wide gap between the performance of small objects and that of medium, large objects. Some studies show that this gap is due to the contradiction between the classification-based backbone and localization. Although the reduction in the feature map size is beneficial for the extraction of abstract features, it will cause the loss of detailed features in the localization as traversing the backbone. Therefore, an interactive multi-scale feature representation enhancement strategy is proposed. This strategy includes two modules: first a multi-scale auxiliary enhancement network is proposed for feature interaction under multiple inputs. We scale the input to multiple scales corresponding to the prediction layers, and only passes through the lightweight extraction module to extract more detailed features for enhancing the original futures. Moreover, an adaptive interaction module is designed to aggregate the features of adjacent layers. This approach provides flexibility in achieving the improvement of small objects detection ability without changing the original network structure. Comprehensive experimental results based on PASCAL VOC and MS COCO datasets show the effectiveness of the proposed method.

在检测领域，小物体的性能与中，大型物体的性能之间存在很大差距。一些研究表明，这种差距是由于基于分类的主干与本地化之间的矛盾所致。尽管减小特征图大小有利于提取抽象特征，但是它会在遍历主干时导致本地化中详细特征的丢失。因此，提出了一种交互式的多尺度特征表示增强策略。该策略包括两个模块：首先，提出了一种多尺度辅助增强网络，用于多个输入下的特征交互。我们将输入缩放到对应于预测层的多个比例，并且仅通过轻量级提取模块来提取更详细的功能以增强原始期货。而且，自适应交互模块被设计为聚集相邻层的特征。该方法在不改变原始网络结构的情况下提供了实现小物体检测能力提高的灵活性。基于PASCAL VOC和MS COCO数据集的综合实验结果证明了该方法的有效性。