Automated Supervision of Personal Protective Equipment Usage

The workers of many fields must follow strict safety rules, and not complying with them may put these workers into severe danger. Oil and Gas workers are subject to hazards and accidents in their workplace frequently. The use of personal protective equipment (PPE) is of summary importance to professionals who work with heavy-duty machinery or in unsafe environments, reducing the risk of serious injuries or even death. The workers are supposed to wear PPE, some of which are gloves, hardhat, and Steel-Toed Boots, during their activities. However, PPE is commonly misused or unused, incurring the need for recurrent inspection. There is no guarantee that the safety equipment is being used correctly, safely and continuously. These threats to the safety of the workers are increased significantly when they are working offshore due to either the harsh conditions they might be working on or inherent dangers that O&G workplaces can offer to the workers such as machinery and dangerous areas with risk of collision and accidents. Detecting the lack of PPE can prevent injuries during work. For this purpose, we propose a surveillance system solution to automatically analyze video footage and detect oil and gas (O&G) workers who are not using adequate protective equipment. This project developed a multi-step detection system using Deep Learning techniques in a pipeline for monitoring workers through camera images. Being able to detect violations to the established rules is an important step towards reducing the impact of incidents and accidents. Using computer vision, deep neural networks, and video footage, we created a web solution for analyzing the imagery in real-time and issuing alerts when a violation happens. For this specific domain, we accomplished the best results by using YOLOv3 as a person detector in conjunction with the Xception network for classification. We achieved 98% precision for the classification step and 78% precision for the joint solution (detection and classification steps) while running in real-time in an NVIDIA Titan X GPU.