利用深度学习进行早期火灾探测

Akshad Jha, Saurabh Vedak, Kapil Mundada, Raj Walnuskar, Utkarsh Chopade, Anand Iyer
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引用次数: 4

摘要

随着近年来基于视觉的火灾探测系统的发展,人类可以设计智能火灾探测系统,这对于提高安全效率和提高整个火灾探测系统的有效性至关重要。实施这项工作的目标是它应该能够生成有关火灾的实时信息。这项工作的目的是克服传统消防系统的缺点。作者已经使用了现代技术,如深度学习,以实现上述目标。利用深度学习技术,火灾探测系统能够实时地对图像中感兴趣的物体进行分类。该系统对给定区域的火灾检测准确率达到80%,同时克服了虚警的产生。通过提取、处理和分析给定帧中的关键信息,系统能够准确地向操作员提供最新的场景信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Fire Detection Using Deep Learning
With the recent advancement in vision-based systems, as a human we can design intelligent fire detection systems which are instrumental for improving the safety efficiency as well as improving the effectiveness of the overall fire detection systems. The objective of implementing this work is that it should be capable of generating real-time information about the fire. The aim behind doing this work is to overcome the drawbacks of traditional firefighting systems. Authors have used the modern technology like deep learning, to achieve the said objective. With the use of Deep Learning fire detection system was able to classify objects of interest from frame in real time. The proposed system is having accuracy of 80% for detecting fire in given region while overcoming the false alarm generation. With this kind of accuracy given system is able to accurately inform operators with up-to-date scene information by extracting, processing, and analysing crucial information from the given frame.
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