Efficient Project Management in Construction Sites to Monitor and Track the Employees using Multi-Modal Deep Learning Model - Dharani, D. Leela; Pattnaik, Manjula; G. N., Naveen Kumar; Anitha Avula, Varagantham; Pattanaik, Balachandra; Maheshwari, Shikha (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In general, past research on automated safety monitoring using computer vision techniques has concentrated on distinct components, accounting for the safety issues individually. This is because there are a wide variety of safety issues that can arise. Recognizing the working status of construction equipment and following the movement of personnel are also instances of this kind of research. A number of researchers have come to the conclusion that it is in their best interest to implement the fundamental principle that supports the operation of a detection-based tracking system is that newly detected items either start new tracks or are mapped to existing tracks for the purpose of identification maintenance over a period of time that has been predetermined. In this paper, an Efficient project management scheme is developed using artificial intelligence. This model enables the construction sites to monitor and track the employees and this uses multi-modal deep learning (MMDL) model to track the safety of the employee. The simulation is performed with movable workers in python to test the efficacy of the MMDL and it is evaluated in terms of accuracy, precision, recall and f-measure. These performance metrics are used in the present study to check if the MMDL model is efficient in classifying the people who are working without any safety. The results show an efficient classification of instances than the other existing state-of-art models.