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

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.


page 3

page 4


Optimizing YOLOv7 for Semiconductor Defect Detection

The field of object detection using Deep Learning (DL) is constantly evo...

Ensemble learning using individual neonatal data for seizure detection

Sharing medical data between institutions is difficult in practice due t...

Investigation of ensemble methods for the detection of deepfake face manipulations

The recent wave of AI research has enabled a new brand of synthetic medi...

Voting based ensemble improves robustness of defensive models

Developing robust models against adversarial perturbations has been an a...

Enhancing Certifiable Robustness via a Deep Model Ensemble

We propose an algorithm to enhance certified robustness of a deep model ...

Towards Adversarially Robust Deepfake Detection: An Ensemble Approach

Detecting deepfakes is an important problem, but recent work has shown t...

DeepFEL: Deep Fastfood Ensemble Learning for Histopathology Image Analysis

Computational pathology tasks have some unique characterises such as mul...

Please sign up or login with your details

Forgot password? Click here to reset