Fish species identification using a convolutional neural network trained on synthetic data

Allken, Vaneeda; Handegard, Nils Olav; Rosen, Shale; Schreyeck, Tiffanie; Morf, Thomas; Malde, Ketil

doi:10.1093/icesjms/fsy147

Cited by 133 publications

(88 citation statements)

References 9 publications

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“…To avoid the need for large amounts of annotated data, general deep structures must be finetuned to improve the effectiveness with which they can identify the pertinent information in the feature space of interest. Accordingly, various DL models for identifying fish species have been developed using a pretrained approach called transfer learning (Siddiqui et al 2017;Lu et al 2019;Allken et al 2019). By fine-tuning pretrained models to perform fish classification using small-scale data sets, these approaches enable the network to learn the features of a target data set accurately and comprehensively (Qiu et al 2018), and achieved sufficiently high accuracy to serve as economical and effective alternatives to manual classification.…”

Section: Species Classificationmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

209

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Species Classificationmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

209

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“…Accurate identification of species is the basis of taxonomic research. Handegard et al [238] used a deep learning model to classify the species present in the image automatically.…”

Section: ) Object Detection In Daily Lifementioning

confidence: 99%

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz

Salam²,

Sheikh³

et al. 2020

IEEE Access

130

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Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R

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“…Klasifikasi spesies ikan [7] komputer visi [6] Identifikasi spesies ikan [8] Gambar 1. Aplikasi ML dalam identifikasi citra ikan.…”

Section: Machine Learningunclassified

Survei Penggunaan Tensorflow pada Machine Learning untuk Identifikasi Ikan Kawasan Lahan Basah

Wiranda

Purba

Sukmawati

2020

Indonesian J. Electron. Instrum. Syst.

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Wetlands are habitats commonly used for fish cultivation. South Kalimantan is one of the provinces that has a wetland area, which is 11,707,400ha, there are 67 rivers and an estimated 200 species of fish. This shows the abundant wealth of fish treasures and economic value. The study of fish identification is an important subject for the preservation of wetland fish. In the field of artificial intelligence, identification can be done using Machine Learning (ML). There are many libraries, a collection of functions that can be used in ML development, one of which is Tensorflow. In this paper, we survey a variety of literature on the use of Tensorflow, as well as datasets, algorithms, and methods that can be used in developing wetland area fish image identification applications.The results of the literature survey show that Tensorflow can be used for the development of fish character identification applications. There are many datasets that can be used such as MNIST, Oxford-I7, Oxford-102, LHI-Animal-Faces, Taiwan marine fish, KTH-Animal, NASNet, ResNet, and MobileNet. Classification methods that can be used to classify fish images include CNN, R-CNN, DCNN, Fast R-CNN, kNN, PNN, Faster R-CNN, SVM, LR, RF, PCA and KFA. Tensorflow provides many models that can be used for image classification, including Inception-v3 and MobileNets, and supports models such as CNN, RNN, RBM, and DBN. To speed up the classification process, image dimensions can be reduced using the MDS, LLE, Isomap, and SE algorithms.

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Fish species identification using a convolutional neural network trained on synthetic data

Cited by 133 publications

References 9 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Survei Penggunaan Tensorflow pada Machine Learning untuk Identifikasi Ikan Kawasan Lahan Basah

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