目标检测是计算机视觉应用的基础, 基于锚框的一些目标检测算法已难以满足目标检测中对目标处理的效率、性能等诸多方面的要求, 而anchor free方法逐渐广泛应用于目标检测. 本文首先重点介绍了CornerNet、CenterNet、FCOS模型的一系列基于关键点的anchor free目标检测方法, 综述了算法思路及其优缺点; 然后分别对基于锚框和基于关键点的目标检测算法在同一个数据集上作了性能比较和分析; 最后对基于关键点的目标检测进行了总结, 并展望了目标检测的未来发展方向.
Object detection is the foundation of computer vision applications. Some object detection algorithms based on anchor boxes have been unable to meet the requirements for object processing efficiency and performance in object detection, and anchor free method is gradually widely used in object detection. This article firstly introduced a series of key-based anchor free object detection methods based on the CornerNet, CenterNet, and FCOS model, and summarized the algorithm ideas, their advantages and disadvantages. Then the performance comparison and analysis of the object detection algorithm based on anchor boxes and key points were performed on the same data set. Finally, the object detection based on key points was summarized, and the future development direction of object detection was prospected.