  Many recently developed object detectors focused on coarse-to-fine framework
which contains several stages that classify and regress proposals from
coarse-grain to fine-grain, and obtains more accurate detection gradually.
Multi-resolution models such as Feature Pyramid Network(FPN) integrate
information of different levels of resolution and effectively improve the
performance. Previous researches also have revealed that localization can be
further improved by: 1) using fine-grained information which is more
translational variant; 2) refining local areas which is more focused on local
boundary information. Based on these principles, we designed a novel boundary
refinement architecture to improve localization accuracy by combining
coarse-to-fine framework with feature pyramid structure, named as Pyramidal
Bounding Box Refinement network(PBRnet), which parameterizes gradually focused
boundary areas of objects and leverages lower-level feature maps to extract
finer local information when refining the predicted bounding boxes. Extensive
experiments are performed on the MS-COCO dataset. The PBRnet brings a
significant performance gains by roughly 3 point of $mAP$ when added to FPN or
Libra R-CNN. Moreover, by treating Cascade R-CNN as a coarse-to-fine detector
and replacing its localization branch by the regressor of PBRnet, it leads an
extra performance improvement by 1.5 $mAP$, yielding a total performance
boosting by as high as 5 point of $mAP$.
