An Edge-Assisted Computer Vision System for Automated Safety Compliance Monitoring in Industrial Facilities

International Journal of Computer Science and Technology | Vol. 3, No. 1, January 2022 | pp. 1–8

Research Article

Title: An Edge-Assisted Computer Vision System for Automated Safety Compliance Monitoring in Industrial Facilities

Names of Authors: Diego Silva¹, Mateo Fernandez², and Isabella Gomez³

Authors’ Affiliations:
¹Department of Automation and Robotics, Universidad de Chile, Santiago, Chile
²Faculty of Engineering, Universidad de los Andes, Bogotá, Colombia
³School of Electrical Engineering, Universidad Nacional Autónoma de México, Mexico City, Mexico

Abstract: Maintaining strict occupational safety compliance, including the proper use of personal protective equipment (PPE) like hard hats and safety vests, is essential for reducing workplace injury rates in heavy industrial plants. Manual compliance auditing by floor supervisors is labor-intensive, intermittent, and prone to oversight, making continuous, automated monitoring solutions highly desirable. This paper presents an edge-assisted computer vision framework designed for real-time PPE detection and safety perimeter monitoring. The core architecture uses an optimized version of the YOLOv5 object detection network, enhanced with depthwise separable convolutions to minimize computational demands. The model processes high-resolution industrial video feeds on localized edge gateways, identifying safety gear compliance and warning when personnel enter hazardous operational zones. To improve detection under dust and variable lighting conditions, a spatial pyramid pooling module is integrated into the model pipeline. Field testing on an industrial processing floor demonstrates a mean average precision (mAP) of 94.6% for hard hat and vest classification. The localized edge processors achieve an inference speed of 32 ms per frame, allowing immediate audio-visual alarms during safety breaches while saving wide-area network bandwidth by avoiding cloud video streaming.

Keywords: Computer vision, Object detection, Industrial safety, Edge computing, Deep learning, Real-time monitoring

Manuscript Timeline: Received: October 14, 2021; Revised: November 20, 2021; Accepted: December 12, 2021; Published: January 4, 2022

Citation: Silva, D., Fernandez, M., & Gomez, I. (2022). An edge-assisted computer vision system for automated safety compliance monitoring in industrial facilities. International Journal of Computer Science and Technology, 3(1), 1–8. DOI: 10.46882/2022/IJCST/000025

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