# 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. instrument monitoring; few-shot learning +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. A New Instrument Monitoring Method Based on Few-Shot Learning. _Appl. Sci._**2023**, _13_, 5185. [[https://doi.org/10.3390/app13085185](https://doi.org/10.3390/app13085185)]([https://doi.org/10.3390/app13085185](https://doi.org/10.3390/app13085185))  
1
Footnote 1: email: {[[https://github.com/journals/j](https://github.com/journals/j)]([https://github.com/journals/j](https://github.com/journals/j))  
difficulty of threshold selection and is also prone to error detection, and omission [10]. Machine learning combined with morphology methods was also largely applied in the field of vision recognition [11; 12; 13; 14; 15]. [PERSON], [PERSON], and [PERSON] [16] used Mask Regional Convolutional Neural Network (Mask-RCNN) to obtain the binary mask for each number and then applied a Back Propagation neural network algorithm to fuse invariant moment information. [PERSON], [PERSON], and [PERSON] [17] proposed image pre-processing and capsules network model for digitalization in Industrial Internet of Things. [PERSON], [PERSON], and [PERSON] [18] used histogram normalization transform to optimize the brightness and enhance the contrast of images, and then You Only Look Once version 3 (YOLOv3) was applied to detect and capture the panel area, and Convolutional Neural Networks (CNNs) were then used to read and predict the characteristic images. In summary, traditional algorithms are vulnerable to environmental interference, such as uneven lightening and noise, so the accuracy is low [15; 19]. When operating the instrumentation, it is important to set the correct parameters. In the experimental scenario presented in this paper, when the centrifuge is operated by the robot arm and the wrong number of revolutions is selected, there is a high risk of overload, downtime, or even machine scrapping. Therefore, the key steps of safety monitoring and operation identification are still largely dependent on manual work. In our experimental scenario, if there is no recognition algorithm with an accuracy rate of more than 98%, we will require a safety officer to monitor whether the parameters are set correctly. With the development of deep learning in the field of machine vision, fully automated instrumentation monitoring has become possible. However, traditional neural networks usually require large datasets for training [20; 21; 22].