  There are two mainstreams for object detection: top-down and bottom-up. The
state-of-the-art approaches mostly belong to the first category. In this paper,
we demonstrate that the bottom-up approaches are as competitive as the top-down
and enjoy higher recall. Our approach, named CenterNet, detects each object as
a triplet keypoints (top-left and bottom-right corners and the center
keypoint). We firstly group the corners by some designed cues and further
confirm the objects by the center keypoints. The corner keypoints equip the
approach with the ability to detect objects of various scales and shapes and
the center keypoint avoids the confusion brought by a large number of
false-positive proposals. Our approach is a kind of anchor-free detector
because it does not need to define explicit anchor boxes. We adapt our approach
to the backbones with different structures, i.e., the 'hourglass' like networks
and the the 'pyramid' like networks, which detect objects on a
single-resolution feature map and multi-resolution feature maps, respectively.
On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer
achieves APs of 53.7% and 57.1%, respectively, outperforming all existing
bottom-up detectors and achieving state-of-the-art. We also design a real-time
CenterNet, which achieves a good trade-off between accuracy and speed with an
AP of 43.6% at 30.5 FPS. https://github.com/Duankaiwen/PyCenterNet.
