Detection of safety equipment in the manufacturing industry using image recognition

Sammanfattning: Safety is an essential part of the paper industry, as the industry can be very hazardous and accidents can lead to serious injuries for the people involved. In order to mitigate and prevent accidents, it has been shown that proactive measures are of great value. One type of proactive measure is the use of Personal Protective Equipment (PPE), such as gloves, hard hats, safety glasses and reflective vests. Despite that it is often required to wear PPE in a work place, it is not always guaranteed and non-usage can affect the safety of workers. To detect unsafe conditions, such as non-usage of PPE, automated video monitoring with image recognition can be useful. The intention of this work is to investigate whether an image recognition model can be created using the cloud service Azure and used in a system that can detect PPE, which in this work is limited to reflective vests. The work results in an artifact using an image recognition model. Additionally, this work examines how the training data can affect the model's performance. It is found that the model can be improved by training the model on images with varying backgrounds, angles, distances, and occlusions. While there are many advantages with automated monitoring, the use of it can raise questions regarding the privacy of the people being monitored and how it can be perceived in a workplace. Therefore, this thesis examines the privacy concerns and attitudes regarding an image recognition system for monitoring. This is accomplished by performing a literature study and interviews with employees at a paper mill. The results reveals challenges with systems for automated monitoring as well as factors that can affect how employees feel about them.