Data-Centric AI Paradigm Based on Application-Driven Fine-Grained Dataset Design

TL;DR

This paper introduces a data-centric paradigm for industrial applications that designs fine-grained datasets based on application needs, significantly reducing false alarms in deep learning models.

Contribution

It proposes a novel application-driven dataset design method that improves false alarm reduction by selecting samples based on essential data features.

Findings

Achieved better false alarm reduction compared to traditional methods.

Collected over 10,000 diverse mask-wearing recognition samples.

Demonstrated effectiveness across various industrial scenarios.

Abstract

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.