  Deep learning has a wide range of applications in industrial scenario, but
reducing false alarm (FA) remains a major difficulty. Optimizing network
architecture or network parameters is used to tackle this challenge in academic
circles, while ignoring the essential characteristics of data in application
scenarios, which often results in increased FA in new scenarios. In this paper,
we propose a novel paradigm for fine-grained design of datasets, driven by
industrial applications. We flexibly select positive and negative sample sets
according to the essential features of the data and application requirements,
and add the remaining samples to the training set as uncertainty classes. We
collect more than 10,000 mask-wearing recognition samples covering various
application scenarios as our experimental data. Compared with the traditional
data design methods, our method achieves better results and effectively reduces
FA. We make all contributions available to the research community for broader
use. The contributions will be available at
https://github.com/huh30/OpenDatasets.
