  Current object detection frameworks mainly rely on bounding box regression to
localize objects. Despite the remarkable progress in recent years, the
precision of bounding box regression remains unsatisfactory, hence limiting
performance in object detection. We observe that precise localization requires
careful placement of each side of the bounding box. However, the mainstream
approach, which focuses on predicting centers and sizes, is not the most
effective way to accomplish this task, especially when there exists
displacements with large variance between the anchors and the targets. In this
paper, we propose an alternative approach, named as Side-Aware Boundary
Localization (SABL), where each side of the bounding box is respectively
localized with a dedicated network branch. To tackle the difficulty of precise
localization in the presence of displacements with large variance, we further
propose a two-step localization scheme, which first predicts a range of
movement through bucket prediction and then pinpoints the precise position
within the predicted bucket. We test the proposed method on both two-stage and
single-stage detection frameworks. Replacing the standard bounding box
regression branch with the proposed design leads to significant improvements on
Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%,
respectively. Code is available at https://github.com/open-mmlab/mmdetection.
