Hand-Monitoring System Using CutMix-Based Synthetic Augmentation for Safety in Factories

Abstract

In this study, we introduce a robust and affordable safety monitoring system using an object-detection algorithm trained using CutMix-based synthetic data. The image-processing algorithm, which is mainly used to improve productivity in the manufacturing industry, has recently emerged as a solution to safety problems at manufacturing sites. With deep learning, various applications have been developed to accurately monitor operator posture, wear safety equipment, and enter and exit hazardous areas. However, there are still limitations in terms of cost and time when using these applications in actual manufacturing sites, especially for small- and medium-sized enterprises. To address these challenges, our system offers the following optimal approaches: 1) leveraging domain-specific data-augmentation methods for efficient hand-monitoring systems, 2) implementing the entire system with accessible hardware and software, and 3) on-site validation at an actual small- and medium-sized enterprise, demonstrating superior performance over traditional augmentation methods. Our training method achieved 98.5% accuracy using just a minute of data, outperforming traditional methods by 28.7%. The system maintains an inference rate of over eight frames per second and can be configured for less than ${\$}200$ . Notably, our system allows rapid training and deployment in diverse environments and has a significant impact on manufacturing safety management.

Keywords

• Safety monitoring
• hand detection
• data augmentation
• CutMix-based synthesis