ABSTRACT
Organizations and industries must ensure safe operation of their facilities, employing rigorous risk management techniques for planning and executing their activities. The use of Personal Protective Equipment (PPE) represents the closest layer of protection to workers, and can considerably reduce the risk of exposure to hazards, being critical for safety in industrial environments. The hazards addressed by protective equipment include physical, acoustic, electrical, heat and chemicals. Despite the substantial efforts in increasing awareness about the benefits of PPE to strive towards zero accident philosophy, operators often neglect its use when not being supervised. However, organizations commonly have surveillance cameras installed which might provide useful visual information on correct usage of PPE. In this context, computer vision is an interdisciplinary field that seeks to automate tasks that the human visual system can do and includes domains of signal and image processing, pattern recognition and artificial intelligence. Moreover, object recognition is a prominent technology from computer vision for finding and identifying objects in an image or video sequence. Then, this work aims to create an automatic PPE detection from surveillance cameras and other video streams using computer vision and machine learning. Equipment such as helmets, safety glasses, earplugs and other garments are checked for whether they are being used by operators in a real-time monitoring, alerting supervisors to prevent accidents and ensure a safer environment.
