Safety Equipment Detection Dataset for Personnel in Construction Areas

In the current construction industry, safety protection has always been an important but often neglected aspect. Given the complex and variable environment of construction sites, especially when personnel safety equipment is inadequate, traditional manual monitoring methods often struggle to be real-time, comprehensive, and accurate. Existing solutions largely rely on manual inspection and local camera monitoring, which have significant limitations in terms of manpower and technology and cannot meet the high safety standards required in dynamic construction environments. This dataset aims to solve the problem of real-time detection of personnel safety equipment in construction areas to improve the level of automation and intelligence in safety monitoring. The dataset is collected through high-definition cameras installed on construction sites, covering real scenarios under different weather and lighting conditions. To ensure data quality, the data undergoes multiple rounds of annotation processing and consistency checks, and the annotation team consists of experts with backgrounds in safety protection and computer vision. Data preprocessing includes image denoising, resolution adjustment, and data augmentation to improve model training effectiveness. The data is stored in JPG format and organized by construction scenario and type of safety equipment. This dataset features high quality, with 95% annotation accuracy and data consistency, using an innovative dynamic annotation feedback mechanism that significantly enhances recognition accuracy. Compared to other safety monitoring datasets on the market, this dataset shows clear advantages in coverage of typical construction scenarios and recognition of types of safety equipment. In addition, the dataset is highly extendable and applicable to different types of construction sites and safety needs.