Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62576

Industrial Safety Gear Detection And Attendance Monitoring System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62576

Volume:

10 |

5 |

May 2026

Pages:

1-11

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

NSA

Nikhil Sachin Agale

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Author Certificate
YRJ

Yash Rahul Jagtaap

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Co-Author Certificate
BBK

Bikesh B. Kumar

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Co-Author Certificate
RRS

Rohan Ramchandra Shelar

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Co-Author Certificate
KYB

Kawal Yogesh Bagal

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Co-Author Certificate

Mentors

BBK

Bikesh B. Kumar

Department of Robotics and Automation

Zeal College of Engineering and Research, Pune, India

Download Mentor Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

In the contemporary industrial landscape, ensuring Personal Protective Equipment (PPE) compliance is a paramount challenge for occupational health and safety. Traditional manual surveillance is inherently flawed due to human fatigue and the logistical impossibility of constant monitoring. This paper proposes an "Intelligent PPE Compliance and Automated Attendance System" that leverages a synergy of Deep Learning (DL), Computer Vision (CV), and the Internet of Things (IoT). By integrating a YOLOv8-based object detection framework with a dlib-driven facial recognition module, the system automates both identity verification and safety auditing. Our architecture utilizes a high- definition optical sensor (Logitech C922) interfaced with a FastAPI-driven microservice and an Arduino-based electromechanical access control unit. Experimental results indicate a Mean Average Precision (mAP) of 91% for PPE detection and a 94% accuracy rate for facial identification, maintaining a low-latency throughput of 150ms per frame. This research underscores a robust, scalable paradigm for "Industry 4.0" safety automation.

KEYWORDS

Keywords— PPE Compliance Intelligent Surveillance System Automated Attendance Deep Learning Computer Vision Internet of Things YOLOv8 Facial Recognition Industry 4.0 Real-Time Monitoring Embedded System

Cite This Article

Nikhil Sachin Agale, Yash Rahul Jagtaap, Bikesh B. Kumar, Rohan Ramchandra Shelar, Kawal Yogesh Bagal, Bikesh B. Kumar, "Industrial Safety Gear Detection And Attendance Monitoring System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-11, May 2026.

Full Article/Conclusion

The Industrial Safety Gear Detection and Attendance Monitoring System provides a practical solution for automating safety compliance and access control in industrial environments. By combining facial recognition with PPE detection, the system ensures that only authorized and properly equipped individuals are allowed entry. This reduces manual supervision, minimizes human error, and strengthens overall workplace safety. The integration of AI-based processing with hardware control enables real-time decision-making and efficient system operation. The modular architecture, consisting of perception, logic, and control layers, allows flexibility for future improvements and scalability. Additionally, the inclusion of logging and cloud storage ensures proper record-keeping, making the system reliable for auditing and monitoring purposes. Overall, the system demonstrates how intelligent automation can enhance both security and safety standards in industries. With its cost-effective design and capability to operate in real-time conditions, it serves as a strong foundation for developing more advanced smart safety systems in the future.

References

1.     Y. Li, H. Wei, Z. Han, J. Huang, and W. Wang, “Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks,” Advances in Civil Engineering, Central South University, Changsha, China, Volume 2020, pp. 1-10, September 2020. 2.     W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A Real- Time Object Detection Method for Constrained Environments,” IEEE Access, Jiangnan University, Wuxi, China, Volume 7, pp. 137 920–137 930, 3.      December 2019. 4.     A. Aboah, B. Wang, U. Bagci, and Y. Adu-Gyamfi, “Real-Time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8,” arXiv preprint, Northwestern University and University of Missouri-Columbia, USA, arXiv:2304.08256, April 2023. 5.     Z. Liu, L. Zhang, and T. Zhao, “Automatic Detection of Safety Helmet Wearing Based on Improved YOLOv5 Algorithm,” Sensors, Shandong University of Science and Technology, Qingdao, China, Volume 22, Article No. 8501, 2022. 6.     R. Rubaiyat, A. Shrestha, K. Waranusast, and J. Li, “Helmet Detection on Construction Sites Using Computer Vision and Deep Learning,” Journal of Safety Research, Chiang Mai University, Thailand, Volume 77, pp. 1-10, 2021. 7.     Y. Zhao, H. Fang, and Y. Luo, “Improving Safety Compliance on Construction Sites Using CNN-Based PPE Detection,” Automation in Construction, Tongji University, Shanghai, China, Volume 138, Article No. 104250, 2022. 8.     J. Terven and D. Cordova-Esparza, "A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond," arXiv preprint, arXiv:2304.00501, April 2023. (Essential for justifying the choice of YOLOv8). 9.      M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510-4520, 2018. (Relevant for the lightweight facial embedding extraction logic). 10.  V. Bazarevsky, Y. Kartynets, A. Vakunov, K. Raveendran, and M. Grundmann, "Blazeface: Sub- millisecond Neural Face Detector on Mobile GPUs," arXiv preprint, arXiv:1907.05047, 2019. (Supports the rationale for real-time face localization at entry points).

Editorial & Review Transparency

Publication Date

5/6/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

7

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...