JellyNet: The convolutional neural network jellyfish bloom detector

Mcilwaine, Ben; Casado, Mónica Rivas

doi:10.1016/j.jag.2020.102279

Cited by 10 publications

(7 citation statements)

References 50 publications

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“…Meanwhile, the integration of DL, unmanned aerial vehicle (UAV) and satellite remote sensing technologies also provide a novel yet effective method for the recognition and detection of aquatic animal. [29][30][31] In conclusion, although the existing aquatic animal research mainly relies on traditional methods to carry out a series of studies on their gene sequence, expression and pathogenic bacteria, 32,33 DL is gradually growing into a more rapid, convenient and accurate method that better supports these researches. This article thoroughly reviews the applications of DL in the field of visual recognition and detection for aquatic animals (including not only marine animals, but also animals bred in fresh water), as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, combining emerging technologies such as big data and cloud computing, DL will help researchers around the world to explore vast aquatic animal resources more conveniently and quickly. Meanwhile, the integration of DL, unmanned aerial vehicle (UAV) and satellite remote sensing technologies also provide a novel yet effective method for the recognition and detection of aquatic animal 29–31 . In conclusion, although the existing aquatic animal research mainly relies on traditional methods to carry out a series of studies on their gene sequence, expression and pathogenic bacteria, 32,33 DL is gradually growing into a more rapid, convenient and accurate method that better supports these researches.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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“…Training aimed to decrease the model loss function value against training data as each step was processed. Model performance was indicated and measured through improvements in accuracy of the model against the test dataset [ 51 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

et al. 2021

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Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

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“…The authors make a series of improvements in the application of the CNN and manage to achieve the result of the accuracy metric at 95.53%. [15] presents JellyNet, a CNN model trained to detect jellyfish blooms from high-resolution images obtained by unmanned aerial vehicles (UAV), also known as drones. The architecture used was based on the VGG-16, and achieved 90% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In sum, despite the similarity between the present work and the cited works, due to the physical characteristics of Portuguese Man-of-war being similar to that of other jellyfish, almost none of the models could be applied in the classification problem presented here, because they have another function. In [14] and [15], the goal is to detect jellyfish, i.e., the intention is to find where and how many animals are in the image. In [6], the goal is also to detect jellyfish and classify them, but using underwater images and among different species than the one studied here.…”

Section: Related Workmentioning

confidence: 99%

Portuguese Man-of-War Image Classification with Convolutional Neural Networks

Carneiro¹,

Nascimento²,

Noernberg³

et al. 2022

Preprint

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Portuguese man-of-war (PMW) is a gelatinous organism with long tentacles capable of causing severe burns, thus leading to negative impacts on human activities, such as tourism and fishing. There is a lack of information about the spatio-temporal dynamics of this species. Therefore, the use of alternative methods for collecting data can contribute to their monitoring. Given the widespread use of social networks, data present on the social media Instagram could represent a promising methodology for monitoring such species. This research aims to validate the use of convolutional neural networks for the Portuguese manof-war images classification allowing the automatation of the recognition of the Istagram records that contain this species. We created a suitable dataset, and we trained three different neural networks: VGG-16, ResNet50, and InceptionV3, with and without a pre-trained step with the ImageNet dataset. We analyzed the results of the different neural networks through the accuracy, precision, recall, and F1 score metrics. The pre-trained ResNet50 network presented the best results , obtaining 94% in the accuracy metric and 95% in the precision, recall, and F1 score metrics. Therefore, we point out that convolutional neural networks can be very effective for recognizing Portuguese man-of-war images from the social media Instagram.

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JellyNet: The convolutional neural network jellyfish bloom detector

Cited by 10 publications

References 50 publications

Deep learning for visual recognition and detection of aquatic animals: A review

Deep learning for visual recognition and detection of aquatic animals: A review

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

Portuguese Man-of-War Image Classification with Convolutional Neural Networks

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