A hybrid deep learning based robust framework for cattle identification

Venkata Sai Praveen Gunda, Harshavardhan Gulla, Vishalteja Kosana, Shivani Janapati
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Abstract

This study proposes a deep learning-based framework for recognizing cows based on images of their muzzle, and faces. This method works well when dealing with missing or false insurance claims. This study proposes a hybrid multi-stage framework consisting of different phases such as augmentation, denoising, enhancement, and classification. The proposed framework is developed by hybridizing convolutional denoising autoencoders (CDAE), least squares generative adversarial network (LS-GAN), Xception feature extractor, and a convolutional neural network (CNN). CDAE is used to initiate the process of denoising noisy images. LS-GAN is used to improve the characteristics of denoised images by enhancing the image by elimination of the residual noise. The Xception is utilised to extract significant and optimal features, and CNN is then used for classification. Various comparative methodologies are used to assess the proposed approach at different phases through several statistical measures. The proposed framework achieved 97.27% accuracy using the test datasets, which is higher than the comparative approaches.
基于混合深度学习的牛识别鲁棒框架
本研究提出了一种基于深度学习的框架,用于根据奶牛的口鼻和面部图像识别奶牛。这种方法在处理丢失或虚假的保险索赔时效果很好。本研究提出了一种混合多阶段框架,包括增强、去噪、增强和分类等不同阶段。该框架由卷积去噪自编码器(CDAE)、最小二乘生成对抗网络(LS-GAN)、异常特征提取器和卷积神经网络(CNN)混合开发而成。CDAE用于启动噪声图像去噪过程。LS-GAN通过消除残差噪声来增强去噪图像,从而改善去噪图像的特性。利用异常提取重要和最优特征,然后使用CNN进行分类。通过几种统计措施,在不同阶段使用各种比较方法来评估拟议的方法。使用测试数据集,该框架的准确率达到97.27%,高于比较方法。
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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