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Intestinal parasitic infections, as a leading causes of morbidity worldwide,still lacks time-saving, high-sensitivity and user-friendly examination method.The development of deep learning technique reveals its broad applicationpotential in biological image. In this paper, we apply several object detectorssuch as YOLOv5 and variant cascadeRCNNs to automatically discriminate parasiticeggs in microscope images. Through specially-designed optimization includingraw data augmentation, model ensemble, transfer learning and test timeaugmentation, our model achieves excellent performance on challenge dataset. Inaddition, our model trained with added noise gains a high robustness againstpolluted input, which further broaden its applicability in practice.

A Robust Ensemble Model for Patasitic Egg Detection and Classification