| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
Joint Sessions of AIM, ICME, & 3DMS
|
| Presentation Title |
Architecture for Developing an Image Recognition Model Workflow for Workplace Safety Application |
| Author(s) |
Kyle Toth, Monika Singhal, Chenn Q. Zhou, Chason Ault, Matt Liddick |
| On-Site Speaker (Planned) |
Kyle Toth |
| Abstract Scope |
This research presents a computer vision-based safety system using multiple models, utilizing the YOLOv8 architecture, to enhance safety by detecting workers and ensuring compliance with personal protective equipment (PPE) requirements. While the second model, trained on industry-specific and open-source data, detects PPE such as safety jackets and helmets with accuracy, the first model identifies workers. Additionally, the system monitors marked static hazard zones, issuing real-time alerts when workers enter these dangerous areas. This multi-model approach offers a practical solution for improving safety protocols and preventing accidents in steel manufacturing. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |