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.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottom-up Path Augmentation •
Convolution •
Cosine Annealing •
CSPDarknet53 •
CutMix •
Darknet-53 •
DropBlock •
FPN •
Global Average Pooling •
Grid Sensitive •
k-Means Clustering •
Label Smoothing •
Logistic Regression •
Max Pooling •
Mish •
PAFPN •
ReLU •
Residual Connection •
Sigmoid Activation •
Softmax •
Softplus •
Spatial Attention Module •
Spatial Pyramid Pooling •
Tanh Activation •
YOLOv3 •
YOLOv4
