Safety clothing and helmets play a crucial role in ensuring worker safety at construction sites. Recently, deep learning methods have garnered significant attention in the field of computer vision for their potential to enhance safety and efficiency in various industries. However, limited availability of high-quality datasets has hindered the development of deep learning methods for safety clothing and helmet detection. In this work, we present a large, comprehensive, and realistic high-quality dataset for safety clothing and helmet detection, which was collected from a real-world chemical plant and annotated by professional security inspectors. Our dataset has been compared with several existing open-source datasets, and its effectiveness has been verified applying some classic object detection methods. The results demonstrate that our dataset is more complete and performs better in real-world settings. Furthermore, we have released our deployment code to the public to encourage the adoption of our dataset and improve worker safety. We hope that our efforts will promote the convergence of academic research and industry, ultimately contribute to the betterment of society.

本文介绍了一种大型的，全面的，逼真的高质量安全服装和头盔数据集，并将其应用于深度学习方法中，其结果表明我们的数据集更加完整，性能更好，以提高工人的安全。

复杂实际工厂安全检查：用于安全服装和头盔检测的高质量数据集