Towards Complex Real-World Safety Factory Inspection: A High-Quality
  Dataset for Safety Clothing and Helmet Detection
Fusheng Yu Xiaoping Wang Jiang Li Shaojin Wu Junjie Zhang Zhigang Zeng
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.