Real-time Robotics Situation Awareness for Accident Prevention in Industry

TL;DR

This paper presents a real-time human-robot interaction system using YOLO object detection to enhance workplace safety by detecting safety gear and issuing alerts to prevent accidents.

Contribution

It introduces a novel real-time safety monitoring approach combining YOLO object detection with robot interaction for accident prevention in industrial environments.

Findings

Effective detection of safety gear violations like helmets and vests.

Successful real-time alerting via Text-to-Speech in a test scenario.

System capable of navigating and identifying risky situations autonomously.

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.