# Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection Source: [Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection]() Collection index: [llms.txt]() Data updated: 2025-11-21T05:01:56.111Z Authors: Solomon Negussie Tesema, El-Bay Bourennane Published: 2022-01-05T23:01:55.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()