A Comparison of the Accuracies of a Convolution Neural Network Built on Different Types of Convolution Layers

Maria Pavlova
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引用次数: 4

Abstract

The development of artificial intelligence aims to increase its reliability, which is determined ambiguously by accuracy. The accuracy increase is key conception in DNN and AI at all. There is a strong relation between training the DNN and accuracy so the training is very important and very complicated. For that reason, looking for high accuracy in fire recognition it is made research a part of which will present in this article. We presented Convolution Neural Network (CNN) created by Python with Keras library can be very useful in early forest fire detection. To improve the accuracy is made private database with fire and smoke pictures exclusively. We tested two CNN architectures and Batch normalization in these architectures. The distribution of each layer's inputs change during training as the parameters of previous layer changed make the training mode complicated. Without claiming a single indicator, the training mode is expressed by trainable and non-trainable parameters related with Batch normalization layer in CNN. The influence of Batch normalization on accuracy are presented in tabular form. The accuracy comparison of two architectures with different layers in CNN is presented in tabular form.
基于不同类型卷积层的卷积神经网络精度比较
人工智能的发展旨在提高其可靠性,而可靠性是由准确性模糊地决定的。提高精度是深度神经网络和人工智能的关键概念。训练深度神经网络和准确率之间有很强的关系,所以训练非常重要也非常复杂。为此,本文对如何提高火力识别的准确率进行了部分研究。我们提出了卷积神经网络(CNN)由Python和Keras库创建,可以在早期森林火灾检测中非常有用。为了提高准确性,建立了火灾和烟雾图像专用数据库。我们测试了两种CNN架构和这些架构中的批处理归一化。在训练过程中,随着前一层参数的变化,每一层输入的分布会发生变化,使得训练模式变得复杂。在不要求单一指标的情况下,训练模式由CNN中与批处理归一化层相关的可训练参数和不可训练参数表示。以表格形式给出了批归一化对精度的影响。以表格的形式给出了CNN中两种不同层的体系结构的精度比较。
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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