  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 application
potential in biological image. In this paper, we apply several object detectors
such as YOLOv5 and variant cascadeRCNNs to automatically discriminate parasitic
eggs in microscope images. Through specially-designed optimization including
raw data augmentation, model ensemble, transfer learning and test time
augmentation, our model achieves excellent performance on challenge dataset. In
addition, our model trained with added noise gains a high robustness against
polluted input, which further broaden its applicability in practice.
