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. Moreover, 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%, 1.6%, and 0.9%, respectively. Code and models will be available at https://github.com/open-mmlab/mmdetection.

本研究提出一种名为SABL的交替方式，其中利用专用的网络分支分别定位边界框的每个边界，以代替传统的中心和大小预测方法，从而实现更准确的物体检测。研究结果表明，在Faster R-CNN、RetinaNet和Cascade R-CNN三种典型检测框架上，利用SABL支持的两步定位方案可以分别提高3.0%、1.7%和0.9%的检测性能。

侧向感知边界定位以提升目标检测的准确性