论文标题
部分可观测时空混沌系统的无模型预测
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
论文作者
论文摘要
在此贡献中，我们使用一种合奏深度学习方法将两个单独的一阶段探测器（即Yolov4和Yolact）的预测结合在一起，目的是检测内窥镜图像中的伪像。这种整体策略使我们能够改善各个模型的鲁棒性，而无需损害其实时计算功能。我们通过训练和测试两个单独的模型和各种集合配置在“内窥镜伪影检测挑战”数据集中证明了方法的有效性。广泛的实验表明，在平均平均精度方面，合奏方法比单个模型和以前的作品的优越性。
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.
