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Sidapara, Dhrumil Nanji (2023) Enhancing Safety in Construction: A Computer Vision Approach for Personal Protective Equipment Detection. Masters thesis, Dublin, National College of Ireland.
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Abstract
Identifying and reducing potential dangers and consequences in the workplace is essential to maintaining employee security throughout production processes. The use of personal protective equipment (PPE) greatly aids in the decrease of sickness and harm connected to the workplace. Modern Deep-learning techniques are used in this research project to recognize and track PPE usage in real time. The suggested approach seeks to get beyond the drawbacks of current models by addressing issues like dim lighting, unfavorable weather on building sites, and complex image backgrounds. Through enhancing present simulations, the study endeavors to simultaneously ascertain all essential PPE components, such as helmets, gloves, safety glasses or masks, protective clothes, and shoes. In order to maximize the number of images analyzed in the second phase, the study technique will make use of the YOLOv5 single-stage recognition of objects framework. The objective of this tactical decision is to optimize effectiveness and agility while recognizing personal protection equipment (PPE) in situations that occur in the moment. The intended outcomes of this strategy include the development and implementation of a flexible and regulated technique for PPE detection. This expected result, with an emphasis on the quick and precise identification of critical safety equipment, is well-positioned to make a substantial contribution to successfully integrating and adoption of cutting-edge technology in guaranteeing risk at work.
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