论文标题
BorderDet：密集对象检测的边界功能
BorderDet: Border Feature for Dense Object Detection
论文作者
论文摘要
密集的对象检测器依赖于在常规图像网格上预测对象的滑动窗口范式。同时，采用网格点上的特征图来生成边界框预测。点功能很方便，但可能缺乏明确的边框信息以进行准确的本地化。在本文中，我们提出了一个名为Border-Align的简单高效的操作员，从边界的极端提取“边框特征”，以增强点特征。基于BorderAlign，我们设计了一种名为BorderDet的新型检测体系结构，该架构明确利用边框信息以进行更强的分类和更准确的定位。使用Resnet-50骨干，我们的方法将单级检测器FCO提高了2.8 AP增益（38.6 V.S. 41.4）。借助Resnext-101-DCN骨干，我们的BorderDet获得了50.3 AP，表现优于现有的最新方法。该代码可在（https://github.com/megvii asturetection/borderdet）上找到。
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate the bounding box predictions. The point feature is convenient to use but may lack the explicit border information for accurate localization. In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature. Based on the BorderAlign, we design a novel detection architecture called BorderDet, which explicitly exploits the border information for stronger classification and more accurate localization. With ResNet-50 backbone, our method improves single-stage detector FCOS by 2.8 AP gains (38.6 v.s. 41.4). With the ResNeXt-101-DCN backbone, our BorderDet obtains 50.3 AP, outperforming the existing state-of-the-art approaches. The code is available at (https://github.com/Megvii-BaseDetection/BorderDet).
