CenterNet Plus: Multiscale Prediction with FPN for CenterNet

Abstract

Although the CenterNet object detection method can be used to achieve high accuracy, it cannot be used to detect objects with overlapping or almost overlapping centers. However, a good trade-off between detection speed and accuracy cannot be achieved when utilizing this method. We improved a series of backbones using CenterNet, output the fusion of multiple feature maps in backbone, and used the feature pyramid network (FPN) mechanism for multiscale object detection. Additionally, we improved the head of the detector and considered adding intersection over union (IoU) branches. Finally, the loss function was improved to improve detection accuracy. Based on the above research, the CenterNet Plus object detection method is proposed in this paper. Through experiments on the COCO dataset, it can be seen that the use of a multiscale FPN mechanism not only solves the problem of center point overlap but also helps improve the accuracy of detection. CenterNet Plus can be used to greatly improve the detection accuracy on the premise of having a higher detection speed.