Analysis of Job Site in Construction using Artificial Neural Network

Analysis of Job Site in Construction using Artificial Neural Network

Publication Date : 2024-02-10
Author(s) :

G. Kharmega Sundararaj, T. Geetha.

           
Article Name :

Analysis of Job Site in Construction using Artificial Neural Network

Abstract :

This paper proposed a model for the analysis of construction that is based on personal protective equipment (PPE). In this context, this study explores the application of Artificial Neural Networks (ANNs) in conjunction with the You Only Look Once (YOLO) architecture for the analysis of job sites in construction projects. Traditional methods of job site analysis often rely on manual monitoring, which is labor-intensive, time-consuming, and prone to errors. The proposed approach harnesses the real-time object detection capabilities of YOLO within an ANN framework to automate the monitoring and analysis process. Through the deployment of YOLO, the system is swiftly identify and track various elements present on the job site, including workers, equipment, materials, and potential hazards. This enables construction managers to obtain timely insights into job site activities, facilitating informed decision-making and proactive risk management.The number of co-workers on a job site might make it difficult to physically do a safety check yet, it is the authority’s primary responsibility to ensure that the workers on the working site are as protected as possible. Because of its high processing speed 45 frames per second YOLO architecture makes real time safety applications possible. The Accuracy Comparison of the YOLO-ANN is 93%, Specificity Comparison of YOLO-ANN is 93.5% and Sensitivity Comparison of YOLO-ANN is 92.5% respectively.

No. of Downloads :

5