论文标题
用于准确对象检测的多机格里德冗余框注释
Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
论文作者
论文摘要
现代领先的对象探测器是从基于CNN的深层骨干分类器网络重新使用的两个阶段或单阶段网络。 Yolov3是一种非常井井的最先进的单发探测器，它采用输入图像并将其分为相等大小的网格矩阵。具有对象的中心的网格单元是负责检测特定对象的网格单元。本文提出了一种新的数学方法，该方法为每个对象分配了多个网格，以准确地拟合边界框预测。我们还提出了一个有效的离线副本数据增强，以进行对象检测。我们提出的方法极大地胜过一些当前的最新对象探测器，可以进一步提高性能。
Modern leading object detectors are either two-stage or one-stage networks repurposed from a deep CNN-based backbone classifier network. YOLOv3 is one such very-well known state-of-the-art one-shot detector that takes in an input image and divides it into an equal-sized grid matrix. The grid cell having the center of an object is the one responsible for detecting the particular object. This paper presents a new mathematical approach that assigns multiple grids per object for accurately tight-fit bounding box prediction. We also propose an effective offline copy-paste data augmentation for object detection. Our proposed method significantly outperforms some current state-of-the-art object detectors with a prospect for further better performance.
