基于卷积神经网络的珍稀动物图像识别

Xinyu Hao, Guangsong Yang, Qiubo Ye, Donghai Lin
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引用次数: 3

摘要

近年来,由于人类的破坏,地球上濒危物种的数量正以惊人的速度增加,保护稀有物种刻不容缓。本文提出了一种基于卷积神经网络(Convolutional Neural Networks, cnn)基本模型的珍稀动物图像识别新方法,通过自主提取训练集中的图像特征,构建珍稀动物图像识别系统。该方法避免了对目标图像进行人工预处理的繁琐过程,可以直接输入原始图像进行识别,比传统的图像识别算法更具可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rare Animal Image Recognition Based on Convolutional Neural Networks
In recent years, due to human destruction, the number of endangered species on the earth is increasing at an alarming rate, and it is urgent to protect the rare species. This paper we propose a new method for rare animal image recognition based on the basic model of Convolutional Neural Networks (CNNs), by which to autonomously extract the image features in the training set and construct an image recognition system to identify rare animals. The method avoids the cumbersome process of manual preprocessing for the target image, and can directly input the original image for recognition, which is more feasible than the traditional image recognition algorithm.
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