  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).
