用YOLO+移动机器人实时识别工装缺失，防患安全事故
Real-time Robotics Situation Awareness for Accident Prevention in Industry
- 基于YOLO检测工人安全装备，结合移动机器人实现实时交互
- 在测试中成功识别无头盔、无反光背心等危险场景并触发语音警告
- 适合工业安全监控场景，对现场人员和设备管理者有实用价值
本研究基于移动机器人与YOLO模型探索人机交互（HRI），以提升工业工作场所的实时情境感知能力并预防事故。通过目标分割技术，提出一种可实时分析工作状态并提供预警信息的方法。为保障工人安全，采用两种YOLO版本（YOLOv8与YOLOv5）与LoCoBot机器人协同，实现对用户行为的监督与交互。实验表明，系统可在测试场景中自主导航，并在检测到如未佩戴安全帽或未穿反光背心等危险情况时，通过文本转语音功能发出警示。结果表明，该系统能有效识别头盔/无头盔、反光背心/无反光背心等风险状态。
原文摘要 · Abstract (English)
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.
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