  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.
