Applying the Haar-cascade Algorithm for Detecting Safety Equipment in Safety Management Systems for Multiple Working Environments

There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build...

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Bibliographic Details
Published in:Electronics (Basel) 2019-10, Vol.8 (10), p.1079
Main Authors: Phuc, Le Tran Huu, Jeon, HyeJun, Truong, Nguyen Tam Nguyen, Hak, Jung Jae
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Efficiency
Factories
Machine learning
Management systems
Principal components analysis
Safety equipment
Safety management
Unmanned aerial vehicles
Warning systems
Working conditions
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Summary:There are many ways to maintain the safety of workers on a working site, such as using a human supervisor, computer supervisor, and smoke–flame detecting system. In order to create a safety warning system for the working site, the machine-learning algorithm—Haar-cascade classifier—was used to build four different classes for safety equipment recognition. Then a proposed algorithm was applied to calculate a score to determine the dangerousness of the current working environment based on the safety equipment and working environment. With this data, the system decides whether it is necessary to give a warning signal. For checking the efficiency of this project, three different situations were installed with this system. Generally, with the promising outcome, this application can be used in maintaining, supervising, and controlling the safety of a worker.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8101079