基于深度卷积神经网络的海洋牧场岩礁性生物图像分类
文献类型：期刊论文
| 作者 | 孙东洋1,2; 刘辉1 ; 张纪红2; 孙利元3; 王清1; 赵建民1
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| 刊名 | 海洋与湖沼
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| 出版日期 | 2021 |
| 卷号 | 52期号:5页码:1160-1169 |
| 关键词 | 海洋牧场 岩礁性生物 图像增强 卷积神经网络 迁移学习 |
| ISSN号 | 0029-814X |
| 其他题名 | CLASSIFICATION OF REEF BIOLOGICAL IMAGES OF MARINE RANCH BASED ON DEEP CONVOLUTION NEURAL NETWORK |
| 文献子类 | 期刊论文 |
| 英文摘要 | Status and change of biological resources in marine ranch can be recorded with underwater video.It is necessary to develop image-based biological classification methods to fully develop machine vision technology.We collected underwater videos with different backgrounds of reef,algal bed,and sediment in Yantai and Weihai areas,Shandong,China,and conduct image enhancement,construction of dataset,and application of three classification model.Effects of various color compensation methods were compared in the enhancement of underwater image in marine ranch area,including color compensation based on green channel,contrast limited adaptive histgram equalization,and so on.An image dataset for reef biological classification was established with manual annotation.In total,23211 images were used,from which 11 species of common fish in reef area(including Lateolabrax japonicus,Liza haematocheilus,Sebastes schlegelii etc.),3 species of echinoderm,and 1 species of crab,were recognized and the images processed.Using the PaddlePaddle2.0 and PaddleX development kits,convolutional neural networks AlexNet,MobileNet V3,and ResNet50 were applied to transfer the learning image classification and verify the robustness of the algorithm on underwater images with noise,from which the accuracy of recognition reached 96.64%,94.75%,and 99.23%,respectively.In addition,the ResNet50 model performed better in robustness with Gaussian noise.Therefore,computer vision technology based on deep learning presented a great application potential in biological resources monitoring in marine ranch,and provide new ideas and methods for the monitoring and management of marine ranch in China. |
| 语种 | 中文 |
| CSCD记录号 | CSCD:7049861 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/34142]  |
| 专题 | 海岸带生物学与生物资源利用重点实验室 海岸带生物资源高效利用研究与发展中心 |
| 作者单位 | 1.中国科学院烟台海岸带研究所,烟台264003; 2.烟台大学,烟台264005; 3.山东省水生生物资源养护管理中心,烟台264000 |
| 推荐引用方式 GB/T 7714 | 孙东洋,刘辉,张纪红,等. 基于深度卷积神经网络的海洋牧场岩礁性生物图像分类[J]. 海洋与湖沼,2021,52(5):1160-1169. |
| APA | 孙东洋,刘辉,张纪红,孙利元,王清,&赵建民.(2021).基于深度卷积神经网络的海洋牧场岩礁性生物图像分类.海洋与湖沼,52(5),1160-1169. |
| MLA | 孙东洋,et al."基于深度卷积神经网络的海洋牧场岩礁性生物图像分类".海洋与湖沼 52.5(2021):1160-1169. |
入库方式： OAI收割
来源：烟台海岸带研究所
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