Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866911275462688768 |
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| author | Tesema, Solomon Negussie Bourennane, El-Bay |
| author_facet | Tesema, Solomon Negussie Bourennane, El-Bay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_01857 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection Tesema, Solomon Negussie Bourennane, El-Bay Computer Vision and Pattern Recognition 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. |
| title | Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2201.01857 |