Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.
翻译:火灾在爆发时具有毁灭性的破坏力,并且以毁灭性的大规模规模影响周围环境。 最大限度地减少其破坏的最佳方法是在火灾发展之前尽快发现火灾。 因此,这项工作考察了AI探测和识别火灾以及减少图像流物体探测探测时间的潜力。 物体探测在过去六年里在速度和准确性上取得了巨大的飞跃,使得实时探测成为可行。 为了我们的目的,我们从几个公共来源收集并贴上适当的数据标签,这些数据被用来训练和评价以流行的YOLOv4天体探测器为基础的若干模型。 我们的侧重点是由一个合作工业伙伴驱动的,在工业仓库中安装我们的系统,其特点是高上限。 这个装置中传统的烟雾探测器的一个缺点是,烟雾必须达到足够的高度。 这项研究中推出的AI模型在相当长的时间里超过了这些探测器,提供了宝贵的预测,有助于进一步减少火灾的影响。