Smoke detection from foggy environment based on color spaces

M. E. Özbek, Uğur Yıldız
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引用次数: 1

Abstract

Detection of smoke from videos captured by surveillance cameras in outdoor environments is one of the useful outcome of Internet of Things (IoT) applications. The potential benefit increases when deep learning (DL) architectures are involved. However, an inherent difficulty is to detect smoke while natural events like fog exists. The effectiveness of color spaces in detection performance has not yet fully evaluated in those architectures. Moreover, the energy and memory requirements of DL architectures may not be applicable for handling IoT implementation demands. Therefore, in this work, a DL architecture with a suitable color space model, applicable for IoT implementations is proposed to detect smoke from videos in foggy environment. By collecting several videos including smoke samples, the performance comparison of popular and the state-of-the-art DL architectures denoted the outperforming result according to both accuracy and memory usage.
基于色彩空间的雾环境烟雾检测
从室外环境中监控摄像头拍摄的视频中检测烟雾是物联网(IoT)应用的有用成果之一。当涉及深度学习(DL)架构时,潜在的好处会增加。然而,一个固有的困难是在雾等自然事件存在的情况下探测烟雾。在这些体系结构中,色彩空间在检测性能方面的有效性尚未得到充分的评估。此外,DL架构的能量和内存需求可能不适用于处理物联网实现需求。因此,在这项工作中,提出了一种适合物联网实现的具有合适色彩空间模型的深度学习架构来检测雾蒙蒙环境下视频中的烟雾。通过收集包括烟雾样本在内的多个视频,对流行的深度学习架构和最先进的深度学习架构进行性能比较,表明根据准确率和内存使用情况,表现优异的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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