An anchor-free object detector with novel corner matching method

Abstract

Recently, remarkable object detection performance has been made by keypoint-based detectors with different keypoint matching strategies. However, the factors considered in most of keypoint matching strategies are not comprehensive, which greatly affects the detection results. In this paper, based on CornerNet we propose a novel anchor-free detector with a brand new keypoint matching method. In our approach, instead of using embedding of each corner to achieve keypoint matching like CornerNet, we predict a matching degree score for each predicted bounding box formed by corners. In order to train model to get accurate matching degree prediction results, we fully consider the location and geometric information of targets, calculating the distance between centers of predicted and ground truth bounding boxes as well as IoU value to decide which pair of top-left and bottom-right corners can form the right bounding box. Besides that, to relieve the problem that categorical predictions associated with corners are not always reliable, we set an additional classification score for each predicted bounding boxes to further promote final detection results. We apply our model on MS-COCO test−dev set for evaluation and the result shows that our method achieves an AP of 46.3 % at single-scale and 47.9% at multi-scale, which are competitive with other state-of-the-art models such as CentripetalNet and Corner Proposal Net (CPN).