Protective Gear Detection Using Convolutional Neural Networks to Enhance Occupational Safety

Ensuring occupational safety in workplaces is a critical concern, especially in dangerous environments where the use of protective gear, such as helmets and other safety equipment, is mandatory. This paper presents a deep learning model for detecting protective gear using Convolutional Neural Networks (CNNs). The model is trained on a public dataset ‘Is an Employee Wearing Safety Gear?’ available on Kaggle. An accuracy of 99% has been achieved. The monitoring of safety compliance by employees can be automated by integrated this technology with surveillance systems for real-time detection, significantly reducing the risks associated with human error in safety enforcement. Future work will include expanding the dataset to incorporate additional classes of protective equipment to scale the system’s applicability across industries.