应用人工智能(AI)改善火灾响应活动

R. Chang, Yan Peng, Seong-Jin Choi, Changjie Cai
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引用次数: 1

摘要

本研究探讨了如何使用实时人工智能(AI)目标检测模型来改善现场事故指挥和火灾响应中的个人责任。我们利用从在线资源和当地消防部门获得的火场图像来训练人工智能物体探测器YOLOv4。因此,实时人工智能物体探测器在利用当地消防部门的图像计算地面消防车和消防员数量时,准确率可以达到90%以上。我们的初步结果表明,人工智能提供了一种创新方法,可以在火灾现场维持消防人员的问责制。通过将摄像头连接到额外的应急管理设备(例如,消防车、救护车或无人机上的摄像头),本研究强调了如何将这项技术广泛应用于各种灾难响应场景,从而改善现场事故消防指挥并加强消防员的责任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Artificial Intelligence (AI) to Improve Fire Response Activities
This research discusses how to use a real-time Artificial Intelligence (AI) object detection model to improve on-site incident command and personal accountability in fire response. We utilized images of firegrounds obtained from an online resource and a local fire department to train the AI object detector, YOLOv4. Consequently, the real-time AI object detector can reach more than ninety percent accuracy when counting the number of fire trucks and firefighters on the ground utilizing images from local fire departments. Our initial results indicate AI provides an innovative method to maintain fireground personnel accountability at the scenes of fires. By connecting cameras to additional emergency management equipment (e.g., cameras in fire trucks and ambulances or drones), this research highlights how this technology can be broadly applied to various scenarios of disaster response, thus improving on-site incident fire command and enhancing personnel accountability on the fireground.
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