The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny objects. Furthermore, FPN lacks spatial perception ability. To address these issues, we propose a novel High Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with two innovative modules. First, we designed a high frequency perception module (HFP) that generates high frequency responses through high pass filters. These high frequency responses are used as mask weights from both spatial and channel perspectives to enrich and highlight the features of tiny objects in the original feature maps. Second, we developed a spatial dependency perception module (SDP) to capture the spatial dependencies that FPN lacks. Our experiments demonstrate that detectors based on HS-FPN exhibit competitive advantages over state-of-the-art models on the AI-TOD dataset for tiny object detection.

翻译：特征金字塔网络（FPN）的引入显著提升了目标检测性能。然而，在微小目标检测方面仍存在重大挑战，因为微小目标的特征在特征图中仅占据极小比例。尽管FPN整合了多尺度特征，但并未直接增强或丰富微小目标的特征。此外，FPN缺乏空间感知能力。为解决这些问题，我们提出了一种新颖的高频与空间感知特征金字塔网络（HS-FPN），其包含两个创新模块。首先，我们设计了高频感知模块（HFP），该模块通过高通滤波器生成高频响应。这些高频响应被用作空间和通道视角的掩码权重，以增强并突出原始特征图中微小目标的特征。其次，我们开发了空间依赖感知模块（SDP），以捕获FPN所缺乏的空间依赖关系。实验表明，基于HS-FPN的检测器在微小目标检测数据集AI-TOD上相比现有先进模型展现出竞争优势。