# Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams

arXiv: https://arxiv.org/abs/2206.07580 · HTML: https://mlsanity.com/paper/2206.07580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures](https://mlsanity.com/paper/2103.15997/) (2021)
- [Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection](https://mlsanity.com/paper/2003.10129/) (2020)

## Abstract (arXiv)

In this contribution we use an ensemble deep-learning method for combining the prediction of two individual one-stage detectors (i.e., YOLOv4 and Yolact) with the aim to detect artefacts in endoscopic images. This ensemble strategy enabled us to improve the robustness of the individual models without harming their real-time computation capabilities. We demonstrated the effectiveness of our approach by training and testing the two individual models and various ensemble configurations on the "Endoscopic Artifact Detection Challenge" dataset. Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.