Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}.

翻译：多尺度特征在目标检测任务中对编码具有尺度差异的物体至关重要。常见的多尺度特征提取策略是采用经典的自顶向下和自底向上的特征金字塔网络。然而，这些方法存在特征信息丢失或退化的问题，削弱了非相邻层级的融合效果。本文提出一种渐近特征金字塔网络（AFPN），以支持非相邻层级间的直接交互。AFPN 首先融合两个相邻的低层特征，然后逐步将高层特征纳入融合过程。通过这种方式，可以避免非相邻层级间较大的语义鸿沟。鉴于在特征融合过程中每个空间位置可能产生多目标信息冲突，进一步采用自适应空间融合操作来缓解这些不一致性。我们将所提出的 AFPN 分别集成到两阶段和单阶段目标检测框架中，并使用 MS-COCO 2017 验证集和测试集进行评估。实验结果表明，与其他最先进的特征金字塔网络相比，我们的方法取得了更具竞争力的结果。代码开源在 \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}。