  Object detection can be regarded as a pixel clustering task, and its boundary
is determined by four extreme points (leftmost, top, rightmost, and bottom).
However, most studies focus on the center or corner points of the object, which
are actually conditional results of the extreme points. In this paper, we
present a new anchor-free dense object detector, which directly regresses the
relative displacement vector between each pixel and the four extreme points. We
also propose a new metric to measure the similarity between two groups of
extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate
this EIoU as a new regression loss. Moreover, we propose a novel branch to
predict the EIoU between the ground-truth and the prediction results, and
combine it with the classification confidence as the ranking keyword in
non-maximum suppression. On the MS-COCO dataset, our method achieves an average
precision (AP) of 39.3% with ResNet-50 and an AP of 48.3% with ResNeXt-101-DCN.
The proposed EPP-Net provides a new method to detect objects and outperforms
state-of-the-art anchor-free detectors.
