# Dynamic boxes fusion strategy in object detection

arXiv: https://arxiv.org/abs/2207.00997 · HTML: https://mlsanity.com/paper/2207.00997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [CBNet: A Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/2107.00420/) (2021)

## Abstract (arXiv)

Object detection on microscopic scenarios is a popular task. As microscopes always have variable magnifications, the object can vary substantially in scale, which burdens the optimization of detectors. Moreover, different situations of camera focusing bring in the blurry images, which leads to great challenge of distinguishing the boundaries between objects and background. To solve the two issues mentioned above, we provide bags of useful training strategies and extensive experiments on Chula-ParasiteEgg-11 dataset, bring non-negligible results on ICIP 2022 Challenge: Parasitic Egg Detection and Classification in Microscopic Images, further more, we propose a new box selection strategy and an improved boxes fusion method for multi-model ensemble, as a result our method wins 1st place(mIoU 95.28%, mF1Score 99.62%), which is also the state-of-the-art method on Chula-ParasiteEgg-11 dataset.