Citation

Saniya M, Amulya B, Sahiti A, Nagarani A, Shanker M. Int. J. Res. Appl. Sci. Eng. Technol. 2022; 10(6): 1269-1278.

Copyright

(Copyright © 2022, IJRASET)

DOI

10.22214/ijraset.2022.44042

PMID

unavailable

Abstract

Visual examination of the plant and in- time memorial to the failure of wearing a safety helmet is of particular significance to avoid injuries of workers at the construction point. Videotape monitoring systems give a large quantum of unshaped image data on- point for this purpose, still, taking a computer vision- based automatic result for real- time discovery. In this regard, we develop a deep learning-based system for the real- time discovery of a safety helmet at the construction point. i.e. presented system uses the YOLO algorithm that's grounded on convolutional neural networks. The trial results demonstrate that the presented machine learning-based model using the YOLO algorithm is able of detecting the unsafe operation of failure of wearing a helmet at the construction point, with satisfactory delicacy and efficiency. However, also the system generates a sound indicating it, If any worker fails to wear helmet. Using color code safety helmets at the workplace is very beneficial. It is also proved that many countries which are using color coding system for safety helmets, have made the working process fast and smooth. There are some specific standard color codes which are already defined for these safety helmets based on job sites or working environment you are present. It will be very helpful for identifying the key people at the time of emergency. This system ables to detect the particular job profile that the employees holding based upon the color of helmet he/she is wearing. This system also includes a module which is able to detect whether the worker is working or idle. we analyze the movement of worker in real-time.

Language: en