A New Instrument Monitoring Method Based on Few-Shot Learning

Abstract

As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However, digital instruments usually have multi-digit features, meaning that the numeric information on the screen is usually a multi-digit number greater than 10. Therefore, the accuracy of recognition with traditional algorithms such as threshold segmentation and template matching is low, and thus instrument monitoring still relies heavily on human labor at present. However, manual monitoring is costly and not suitable for risky experimental environments such as those involving radiation and contamination. The development of deep neural networks has opened up new possibilities for fully automated instrument monitoring; however, neural networks generally require large training datasets, costly data collection, and annotation. To solve the above problems, this paper proposes a new instrument monitoring method based on few-shot learning (FLIMM). FLIMM improves the average accuracy (ACC) of the model to 99% with only 16 original images via effective data augmentation method. Meanwhile, due to the controllability of simulated image generation, FLIMM can automatically generate annotation information for simulated numbers, which greatly reduces the cost of data collection and annotation.