  Localization Quality Estimation (LQE) is crucial and popular in the recent
advancement of dense object detectors since it can provide accurate ranking
scores that benefit the Non-Maximum Suppression processing and improve
detection performance. As a common practice, most existing methods predict LQE
scores through vanilla convolutional features shared with object classification
or bounding box regression. In this paper, we explore a completely novel and
different perspective to perform LQE -- based on the learned distributions of
the four parameters of the bounding box. The bounding box distributions are
inspired and introduced as "General Distribution" in GFLV1, which describes the
uncertainty of the predicted bounding boxes well. Such a property makes the
distribution statistics of a bounding box highly correlated to its real
localization quality. Specifically, a bounding box distribution with a sharp
peak usually corresponds to high localization quality, and vice versa. By
leveraging the close correlation between distribution statistics and the real
localization quality, we develop a considerably lightweight Distribution-Guided
Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2.
To our best knowledge, it is the first attempt in object detection to use a
highly relevant, statistical representation to facilitate LQE. Extensive
experiments demonstrate the effectiveness of our method. Notably, GFLV2
(ResNet-101) achieves 46.2 AP at 14.6 FPS, surpassing the previous
state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO
{\tt test-dev}, without sacrificing the efficiency both in training and
inference. Code will be available at https://github.com/implus/GFocalV2.

Computer Vision and Pattern Recognition