{"title":"基于深度学习技术的河口鱼类物种自动分类系统","authors":"H. Tejaswini;M. M. Manohara Pai;Radhika M. Pai","doi":"10.1109/ACCESS.2024.3468438","DOIUrl":null,"url":null,"abstract":"Fish classification (FC) is crucial in various domains, including fishery management and ecological research. Traditional FC methods rely mainly on morphological criteria such as body shape and patterns. Although these methods are useful, they require expert knowledge and are prone to subjective interpretation. Recent advances in technology and the availability of datasets have allowed deep learning (DL) techniques to be used in fish species classification. These methods automatically extract relevant features from fish images and categorize them into species groupings. Traditional DL models, however, have difficulties capturing long-range dependencies and require fixed input sizes, making them less adaptive when working with images with varying proportions. The Vision Transformer (ViT) addresses these constraints by utilizing the transformer model’s self-attention mechanisms. So, in this study, a ViT is used to solve the FC problem. The performance of ViT is assessed against pre-trained models, VGG16, VGG19, DenseNet121, ResNet50v2, InceptionV3, InceptionResNetV2, and Xception. The experiments make use of a curated Estuarine Fish species dataset (EFD). In this study, ViT outperformed state-of-the-art literature by achieving 99.04% and 100% accuracy without and with augmentation, respectively. The presented research is tailored to the task of recognizing estuarine fish species that are useful in the aquaculture domain. Additionally, our research aligns with the objectives of Sustainable Development Goals (SDGs) 2 and 14. This emphasises the broader societal and environmental implications of our work, emphasizing its potential to positively impact food security and aquaculture ecosystem sustainability.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695087","citationCount":"0","resultStr":"{\"title\":\"Automatic Estuarine Fish Species Classification System Based on Deep Learning Techniques\",\"authors\":\"H. Tejaswini;M. M. Manohara Pai;Radhika M. Pai\",\"doi\":\"10.1109/ACCESS.2024.3468438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fish classification (FC) is crucial in various domains, including fishery management and ecological research. Traditional FC methods rely mainly on morphological criteria such as body shape and patterns. Although these methods are useful, they require expert knowledge and are prone to subjective interpretation. Recent advances in technology and the availability of datasets have allowed deep learning (DL) techniques to be used in fish species classification. These methods automatically extract relevant features from fish images and categorize them into species groupings. Traditional DL models, however, have difficulties capturing long-range dependencies and require fixed input sizes, making them less adaptive when working with images with varying proportions. The Vision Transformer (ViT) addresses these constraints by utilizing the transformer model’s self-attention mechanisms. So, in this study, a ViT is used to solve the FC problem. The performance of ViT is assessed against pre-trained models, VGG16, VGG19, DenseNet121, ResNet50v2, InceptionV3, InceptionResNetV2, and Xception. The experiments make use of a curated Estuarine Fish species dataset (EFD). In this study, ViT outperformed state-of-the-art literature by achieving 99.04% and 100% accuracy without and with augmentation, respectively. The presented research is tailored to the task of recognizing estuarine fish species that are useful in the aquaculture domain. Additionally, our research aligns with the objectives of Sustainable Development Goals (SDGs) 2 and 14. 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Automatic Estuarine Fish Species Classification System Based on Deep Learning Techniques
Fish classification (FC) is crucial in various domains, including fishery management and ecological research. Traditional FC methods rely mainly on morphological criteria such as body shape and patterns. Although these methods are useful, they require expert knowledge and are prone to subjective interpretation. Recent advances in technology and the availability of datasets have allowed deep learning (DL) techniques to be used in fish species classification. These methods automatically extract relevant features from fish images and categorize them into species groupings. Traditional DL models, however, have difficulties capturing long-range dependencies and require fixed input sizes, making them less adaptive when working with images with varying proportions. The Vision Transformer (ViT) addresses these constraints by utilizing the transformer model’s self-attention mechanisms. So, in this study, a ViT is used to solve the FC problem. The performance of ViT is assessed against pre-trained models, VGG16, VGG19, DenseNet121, ResNet50v2, InceptionV3, InceptionResNetV2, and Xception. The experiments make use of a curated Estuarine Fish species dataset (EFD). In this study, ViT outperformed state-of-the-art literature by achieving 99.04% and 100% accuracy without and with augmentation, respectively. The presented research is tailored to the task of recognizing estuarine fish species that are useful in the aquaculture domain. Additionally, our research aligns with the objectives of Sustainable Development Goals (SDGs) 2 and 14. This emphasises the broader societal and environmental implications of our work, emphasizing its potential to positively impact food security and aquaculture ecosystem sustainability.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.