[1]袁小平,石 慧.基于迁移学习的鱼类识别方法研究[J].计算机技术与发展,2021,31(04):52-56.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 009]
　YUAN Xiao-ping,SHI Hui.Research on Fish Recognition Method Based on Transfer Learning[J].,2021,31(04):52-56.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 009]
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基于迁移学习的鱼类识别方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年04期
页码:
52-56
栏目:
图形与图像
出版日期:
2021-04-10

文章信息/Info

Title:
Research on Fish Recognition Method Based on Transfer Learning
文章编号:
1673-629X(2021)04-0052-05
作者:
袁小平; 石 慧
中国矿业大学,江苏 徐州 221000
Author(s):
YUAN Xiao-ping; SHI Hui
China University of Mining and Technology,Xuzhou 221000,China
关键词:
鱼类识别; 迁移学习; AlexNet; 卷积神经网络; 支持向量机; 微调
Keywords:
fish recognition; transfer learning; AlexNet; convolutional neural network; support vector machine; fine-tuning
分类号:
TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 04. 009
摘要:
近些年水下图像资源已经引起海洋生态学家对鱼类种群研究的关注,鱼品种的识别既是海洋鱼类资源探测的第一步,也是有效开发利用海洋资源的重要基础, 而自动化的鱼品种的分类识别也将提高在该领域的研究效率。 利用迁移学习的强大能力和巨大优势,不需要手动计算图像特征,神经网络使用原始图像作为输入,将其运用到鱼类图像的识别方面。 该文提出了一种基于 AlexNet 预训练模型和迁移学习技术的鱼类识别方法,使用预训练的 AlexNet 网络从鱼类数据集的前景图像中提取特征,对网络模型进行微调,最后利用线性支持向量机分类器完成分类。 通过研究卷积神经网络的架构,激活函数和数据增强对识别结果的影响,经过大量对比实验验证了所提出的网络模型的有效性,提高了鱼类识别的准确率。
Abstract:
In recent years,underwater image resources have attracted the attention of marine ecologists on fish population research. Identification of fish species is not only the first step in the exploration of marine fish resources,but also an important basis for the effective development and utilization of marine resources. The automatic classification and identification of fish species will also improve research efficiency in this field. Taking advantage of the powerful ability and great advantage of transfer learning,there is no need to manually calculate image features. Neural network uses the original image as input and applies it to fish image recognition. We propose a method of fish recognition based on AlexNet pre-trained model and transfer learning technology. The pre-trained AlexNet network is used to extract features from the foreground images of fish data sets, fine - tune the network model, and finally use the linear support vector machine classifier to complete the classification. By studying the influence of the structure of convolutional neural network,activation function and data enhancement on the recognition results,extensive comparative experiments verify the effectiveness of the network model proposed and improve the accuracy of fish recognition.

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更新日期/Last Update: 2020-04-10