基于深度神经网络和图像处理的火灾探测新方法

Yuning Wang
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引用次数: 0

摘要

计算机视觉和深度神经网络的发展使得对物体(如火)的准确分类成为可能。火灾会对人类造成严重威胁,因此预防火灾成为社会关注的主要问题。本文将经过训练的ResNet-50模型与基于rgb的火灾分析相结合,以达到更高的精度。总之,ResNet-50模型对图像的分类速度很快,而RGB模型可以根据实际环境进行调整。它们相辅相成。在图像中,RGB模型正确地检测到火焰,而ResNet-50模型的准确率达到96.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Fire Detection Method Based on Deep Neural Network and Image Processing
The development of computer vision and deep neural networks has enabled the accurate classification of objects like fire. Fire can pose a serious threat to humans, so the prevention of fire becomes a major concern for society. In this paper, a trained ResNet-50 model is combined with an RGB-based analysis of fire to achieve higher accuracy. In short, the ResNet-50 model categorizes images quickly, while the RGB model can be adjusted based on the actual environment. They complement each other. In images, the RGB model correctly detects the fire, whereas the ResNet-50 model achieves 96.2% accuracy.
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