The main challenge for object detection in aerial images is small object detection. Most existing methods use feature fusion strategies to enhance small object features in shallow layers but ignore the problem of inconsistent small object local region responses between feature layers, namely the semantic gap, which may lead to underutilization of small object information in multiple feature layers. To lift the above limitations, we propose a scale enhancement module that adaptively passes valuable small object features in different feature layers to shallow layers to alleviate the semantic gap problem. In particular, the module includes the novel fine-coarse self-attention mechanism, which captures global contextual information by performing strong interaction of pixel-level information at the local scale and weak interaction of region-level information at the global scale. In addition, the anchor assignment strategy based on the Intersection over Union (IoU) metric is not favorable for small objects as the IoU metric for small objects has a lower tolerance for position deviation compared to large ones. For this reason, we design the dynamic anchor assignment strategy with a scale-insensitive metric to assign adequate anchors to small objects. Extensive experiments on three aerial datasets demonstrate the effectiveness and adaptability of our method.

航空图像中物体检测的主要挑战是小物体检测。现有方法大多数采用特征融合策略来增强浅层小目标特征，但忽略了特征层之间小目标局部区域响应不一致的问题，即语义间隙，这可能导致多个特征层中小目标信息的利用不足。为了克服上述限制，我们提出了一种尺度增强模块，该模块自适应地将不同特征层中有价值的小对象特征传递到浅层，以缓解语义差距问题。特别是，该模块包括新颖的细粗自注意力机制，该机制通过在局部尺度上执行像素级信息的强交互和在全局尺度上执行区域级信息的弱交互来捕获全局上下文信息。此外，基于交并集（IoU）度量的锚点分配策略对于小对象并不有利，因为与大对象相比，小对象的 IoU 度量对位置偏差的容忍度较低。为此，我们设计了具有尺度不敏感度量的动态锚点分配策略，为小对象分配足够的锚点。对三个航空数据集的广泛实验证明了我们方法的有效性和适应性。