Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection

• URL: http://arxiv.org/abs/2201.01857v1
• Date: Wed, 5 Jan 2022 23:01:55 GMT
• Title: Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
• Authors: Solomon Negussie Tesema, El-Bay Bourennane
• Abstract summary: YOLOv3 is a state-of-the-art one-shot detector that takes in an input image and divides it into an equal-sized grid matrix. This paper presents a new mathematical approach that assigns multiple grids per object for accurately tight-fit bounding box prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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