  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.
