Detection of Multiscale Center Point Objects Based on Parallel Network

Anchor-based detectors are widely used in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet. Most of which are invalid. Although the anchor-free method can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes a multi-scale center point object detection method based on parallel network to further reduce the number of useless anchor boxes. This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53. Combining feature pyramid and CIOU loss function this algorithm is trained and tested on MSCOCO dataset, increasing the detection rate of target location and the accuracy rate of small object detection. Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy speed.