Dolphin movement direction recognition using contour-skeleton information

Qi, Hong; Xue, Mingzhu; Peng, Xianglong; Wang, Chong; Jiang, Yu

doi:10.1007/s11042-020-09659-y

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…In the past, the observation and monitoring of aquatic animals were mainly carried out by acoustic sensing, satellite tracking, accelerometers, drones and so on. 66 With the development of image processing and machine learning technologies, PCA and SVM are commonly used to extract the colour, texture and shape features from the captured images of aquatic animals to realise automatic object recognition and detection. [67][68][69][70][71] However, the underwater environment is complex, and traditional image processing methods cannot solve the problem well when the colour of the object is distorted or similar to the environment.…”

Section: Image Detectionmentioning

confidence: 99%

“…There are abundant biological resources in the ocean, especially the underwater nekton such as fish, shrimp, sea cucumber, lobster, jellyfish, whales and sharks. In the past, the observation and monitoring of aquatic animals were mainly carried out by acoustic sensing, satellite tracking, accelerometers, drones and so on 66 . With the development of image processing and machine learning technologies, PCA and SVM are commonly used to extract the colour, texture and shape features from the captured images of aquatic animals to realise automatic object recognition and detection 67–71 .…”

Section: Applicationsmentioning

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: Image Detectionmentioning

confidence: 99%

Section: Applicationsmentioning

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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“…However, there has been a scarcity of research on using pose estimation techniques to recognize dolphin skeletons. The only study conducted in this area was by Qi et al [30], who used OpenPose technology for dolphin skeleton detection. Nevertheless, there has been no research utilizing DeepLabCut for similar dolphin skeleton detection.…”

Section: Limitations Of Current Approachesmentioning

confidence: 99%

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Tseng,

Hsu,

Wang

et al. 2024

JMSE

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Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. This study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 key points using an open-source tool called DeepLabCut, which accurately helped mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the long short-term memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior exceeded 90%.

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Tennis action recognition and evaluation with inertial measurement unit and SVM

Gao,

Zhang

2024

Systems and Soft Computing

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Dolphin movement direction recognition using contour-skeleton information

Cited by 3 publications

References 27 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

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Tennis action recognition and evaluation with inertial measurement unit and SVM

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