  For most of the anchor-based detectors, Intersection over Union(IoU) is
widely utilized to assign targets for the anchors during training. However, IoU
pays insufficient attention to the closeness of the anchor's center to the
truth box's center. This results in two problems: (1) only one anchor is
assigned to most of the slender objects which leads to insufficient supervision
information for the slender objects during training and the performance on the
slender objects is hurt; (2) IoU can not accurately represent the alignment
degree between the receptive field of the feature at the anchor's center and
the object. Thus during training, some features whose receptive field aligns
better with objects are missing while some features whose receptive field
aligns worse with objects are adopted. This hurts the localization accuracy of
models. To solve these problems, we firstly design Gaussian Guided IoU(GGIoU)
which focuses more attention on the closeness of the anchor's center to the
truth box's center. Then we propose GGIoU-balanced learning method including
GGIoU-guided assignment strategy and GGIoU-balanced localization loss. The
method can assign multiple anchors for each slender object and bias the
training process to the features well-aligned with objects. Extensive
experiments on the popular benchmarks such as PASCAL VOC and MS COCO
demonstrate GGIoU-balanced learning can solve the above problems and
substantially improve the performance of the object detection model, especially
in the localization accuracy.
