RDT-FSDet: Few-shot object detection for rapid antigen test

Abstract

Abstract Objective: Manual verification of RDT (rapid diagnostic test) results is a time-consuming task; therefore, it is essential to introduce an object detection model into RDT result recognition to reduce the time involved. To address these problems, a detector that can rapidly adapt to different RDT results in various regions is important. Methods: We employed the few-shot object detection strategy and trained the Faster R-CNN detector with the mainland dataset as the base class, followed by fine-tuning with the few-shot approach on the Macau RDT result dataset. Moreover, we introduced two novel data augmentation methods, namely the Light Simulation Mask method and Synthetic Positive Samples for an unbalanced dataset, to increase the sample size and balance the dataset of the RDT detection task. Result: Compared to LightR-YOLOv5, RDT-FSDet achieved mAP of 91.18 and recall of 93.59 on the Macau RDT dataset, demonstrating that this model can rapidly adapt to RDT results in different regions. The inference time of RDT-FSDet for each RDT result was 0.14 seconds, which can save approximately 90% of the detection time compared to manual screening. Conclusion: In addition to its application in the context of the COVID-19 pandemic, this model can also be used as a general small-sample detection model. RDT-FSDet can be applied to the detection tasks of other small datasets such as managing and analyzing detection results in other or future epidemics.