AI driven constrution site safety management using computer vision and Machine learning
No Thumbnail Available
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
UMT. Lahore
Abstract
The issue of safety is multifaceted at construction sites, needing strong measures for risk
mitigation and lightening loads on personnel and assets. In this regard, the confluence of
advanced technologies, such as computer vision and artificial intelligence, has increasingly
been applied to bring novel solutions in the augmentation of safety protocols. With the
advancement in object detection models, mainly based on the YOLO (You Only Look Once)
architecture, that has been the strong way toward the automation of the detection and
localization of safety-critical elements within the construction environment. This research
introduces an intricately fine-tuned YOLOv8 model, finely tuned for detection of construction
safety. Therefore, this work introduces an advanced approach to training and techniques for
fine-tuning a YOLOv8 model capable of detecting a wide range of safety entities, viz. hardhats,
masks, safety vests, personnel, machinery, and vehicles in static images and video feeds. The
YOLOv8 model is real-time and highly precise and accurate in inference, aiming to set new
benchmarks in safety at the construction site—that of the rare accident, satisfied safety
regulators, and continuous good health of its workers and assets. This article, therefore,
presents the multifaceted capabilities and disruptive potential that shape the YOLOv8 model
in the augmentation of safety frameworks and risk mitigation across the dynamic and
challenging landscapes of the construction environment. This research is fulfilling primarily
sustainable development goal number 3 i.e. good health and wellbeing and secondarily
sustainable development goal number 9 i.e. industry, innovation and infrastructure.
Index Terms