Probe - Media and Communication Studies

The Object Detection of Anchor Free—A Survey

Rui Huo

School of Physics and Electronic Information, Yan’an University

Shuili Zhang

School of Physics and Electronic Information, Yan’an University

Huaiyuan Sun

School of Physics and Electronic Information, Yan’an University

Xve Tian

School of Physics and Electronic Information, Yan’an University

DOI: https://doi.org/10.59429/pmcs.v6i3.7244

Keywords: Deep learning; Object detection; Image recognition; Anchor

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Abstract

Since Ross Girshick introduced deep learning to object detection and proposed RCNN networks in 2014, the field of object detection has been blasting ahead. Deep learning-based object detection algorithms typically classify and regress region proposals. In the one-stage detector, these region proposals are the anchor boxes generated by the sliding window approach. In this paper, we first give a brief overview of anchors and compare the advantages and disadvantages of both anchor base and anchor-free object detection. Then, we collect and organize the anchor-free object detection algorithms (such as CenterNet, FCOS, FSAF, etc.) that have received much attention and use in recent years. We also present a detailed description of the anchor-free object detection algorithms in three parts (keypoint-based, Segmentation-based, and YOLO series).

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