Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In contemporary industrial environments, ensuring employee safety is a critical concern. This paper presents an intelligent safety system that integrates facial recognition and helmet detection technologies to automate the enforcement of workplace safety protocols. The system employs strategically deployed cameras, facial feature extraction using computer vision libraries, and deep learning
Models based on TensorFlow to identify individuals and verify helmet usage. By combining these technologies, the proposed solution enhances real-time monitoring, improves safety compliance, and mitigates the risk of workplace accidents.
"Real-Time Safety. Compliance Monitoring with YOLO for Helmet and Face Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b627-b631, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505169.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator