基于卷积神经网络的DUICM深水图像分类模型

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS International Journal of Grid and High Performance Computing Pub Date : 2020-07-01 DOI:10.4018/ijghpc.2020070106
Manimaran Aridoss, Chandramohan Dhasarathan, A. Dumka, L. Jayakumar
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引用次数: 8

摘要

水下图像的分类是一项具有挑战性的任务,因为波长相关的光传播、吸收和色散会扭曲图像的可见性,从而在困难的操作环境中产生低对比度和退化的图像。深度学习算法适合对浑浊图像进行分类,因为使用了softmax激活函数进行分类,并且最小化了交叉熵损失。本文提出的深海图像分类模型(DUICM)采用卷积神经网络(CNN)这一机器学习算法对水下图像进行自动分类。它有助于训练图像并应用分类技术对从基准浑浊图像数据集中选择的特征进行浑浊图像分类。基于CNN模型的多幅水下图像训练系统,该模型独立于每种水下图像的形成。实验结果表明,DUICM对浑浊水下图像具有较好的分类精度。利用不同特征的浑浊图像验证了所提神经网络模型的泛化能力。
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DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks
Classification of underwater images is a challenging task due to wavelength-dependent light propagation, absorption, and dispersion distort the visibility of images, which produces low contrast and degraded images in difficult operating environments. Deep learning algorithms are suitable to classify the turbid images, for that softmax activation function used for classification and minimize cross-entropy loss. The proposed deep underwater image classification model (DUICM) uses a convolutional neural network (CNN), a machine learning algorithm, for automatic underwater image classification. It helps to train the image and apply the classification techniques to categorise the turbid images for the selected features from the Benchmark Turbid Image Dataset. The proposed system was trained with several underwater images based on CNN models, which are independent to each sort of underwater image formation. Experimental results show that DUICM provides better classification accuracy against turbid underwater images. The proposed neural network model is validated using turbid images with different characteristics to prove the generalization capabilities.
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来源期刊
CiteScore
1.70
自引率
10.00%
发文量
24
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