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Real-time Robotics Situation Awareness for Accident Prevention in Industry

Juan M. Deniz, Andre S. Kelboucas, Ricardo Bedin Grando

TL;DR

This study explores human-robot interaction based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace and can conclude that the system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.

Abstract

This study explores human-robot interaction (HRI) based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace. Using object segmentation, we propose an approach that is capable of analyzing these situations in real-time and providing useful information to avoid critical working situations. In the industry, ensuring the safety of workers is paramount, and solutions based on robots and AI can provide a safer environment. For that, we proposed a methodology evaluated with two different YOLO versions (YOLOv8 and YOLOv5) alongside a LoCoBot robot for supervision and to perform the interaction with a user. We show that our proposed approach is capable of navigating a test scenario and issuing alerts via Text-to-Speech when dangerous situations are faced, such as when hardhats and safety vests are not detected. Based on the results gathered, we can conclude that our system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.

Real-time Robotics Situation Awareness for Accident Prevention in Industry

TL;DR

This study explores human-robot interaction based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace and can conclude that the system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.

Abstract

This study explores human-robot interaction (HRI) based on a mobile robot and YOLO to increase real-time situation awareness and prevent accidents in the workplace. Using object segmentation, we propose an approach that is capable of analyzing these situations in real-time and providing useful information to avoid critical working situations. In the industry, ensuring the safety of workers is paramount, and solutions based on robots and AI can provide a safer environment. For that, we proposed a methodology evaluated with two different YOLO versions (YOLOv8 and YOLOv5) alongside a LoCoBot robot for supervision and to perform the interaction with a user. We show that our proposed approach is capable of navigating a test scenario and issuing alerts via Text-to-Speech when dangerous situations are faced, such as when hardhats and safety vests are not detected. Based on the results gathered, we can conclude that our system is capable of detecting and informing risk situations such as helmet/no helmet and safety vest/no safety vest situations.
Paper Structure (11 sections, 8 figures, 2 tables)

This paper contains 11 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. LoCoBot and ROS2
  5. YOLO
  6. Safety Equipment Dataset
  7. Real Time Implementation
  8. Experimental Setup
  9. Experimental Results
  10. Results
  11. Conclusion

Figures (8)

  • Figure 1: Our proposed system in a helmet and safety vest detection evaluation.
  • Figure 2: LoCoBot - Robot used for this research
  • Figure 3: Scenario used for the tests, showing the test subject with the safety equipment.
  • Figure 4: Detection node scheme
  • Figure 5: F1-Curve, Precision-Confidence, Precision-Recall and Recall-Confidence Curves
  • ...and 3 more figures