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Harnessing the Power of Deep Learning to Save Animals

B. V., Padma Bonde
2017 International Journal of Computer Applications  
system more efficient and optimized .This paper discusses the expansion of deep learning towards the welfare of animal life considering ways to save them and monitoring their count and health conditions  ...  Techniques like image classification and object detection can provide plethora of information that can assist in conducting study on various aspects by identifying and classifying these animal species  ...  Using deep learning neural network model trained with these collected images and videos can track, pinpoint and even predict the paths of animals, vehicles and poachers, learning more and more as time  ... 
doi:10.5120/ijca2017915864 fatcat:oshzl7ry5jgk5lxca2utrpwdx4

Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications [article]

Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs
2016 arXiv   pre-print
Detection-classification for passive acoustics is a data-mining strategy and our goals are aligned with best practices that appeal to the general data mining and machine learning communities where the  ...  With this basic deficiency recognized at the forefront, portions of the grant were dedicated to fostering deep-learning by way of international competitions (kaggle.com) meant to attract deep-learning  ...  to support large amounts of data required for deep recognition and other advanced technologies.  ... 
arXiv:1605.00982v2 fatcat:h2yvqur3srhulotr5bju3yoho4

Setting the stage for the machine intelligence era in marine science

Cigdem Beyan, Howard I Browman
2020 ICES Journal of Marine Science  
The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex  ...  It has significant advantages compared with manual analyses that are labour intensive and require considerable time.  ...  Acknowledgements We are grateful to the authors for their enthusiasm and engagement in this initiative.  ... 
doi:10.1093/icesjms/fsaa084 fatcat:zwzvuhlpmbb7bi2lv6xhtg52lm

Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method

Fenglei Han, Jingzheng Yao, Haitao Zhu, Chunhui Wang
2020 Mathematical Problems in Engineering  
The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing.  ...  This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision.  ...  framework is named "deep learning."  ... 
doi:10.1155/2020/3937580 fatcat:qvmvhzauybhafgu5prmaqij2qq

Automated Video Surveillance for the Study of Marine Mammal Behavior and Cognition

Jeremy Karnowski, Christine Johnson, Edwin Hutchins
2016 Animal Behavior and Cognition  
Advances in boat-based, aerial, and underwater recording platforms provide opportunities to document the behavior of marine mammals and create massive datasets.  ...  Systems for detecting and tracking social marine mammals, including dolphins, can provide data to help explain their social dynamics, predict their behavior, and measure the impact of human interference  ...  Working with aerial photos, recent advances in deep learning have allowed for even more effective automated identification.  ... 
doi:10.12966/abc.05.11.2016 fatcat:4pbtwqjxmfamdhfqc7bluktdk4

Fish species recognition using transfer learning techniques

Jaisakthi Seetharani Murugaiyan, Mirunalini Palaniappan, Thenmozhi Durairaj, Vigneshkumar Muthukumar
2021 IJAIN (International Journal of Advances in Intelligent Informatics)  
of marine animals.  ...  To overcome this problem, we propose a transfer learning technique using a pre-trained model that uses underwater fish images as input and applies a transfer learning technique to detect the fish species  ...  Acknowledgment The authors would like to thank the management of VIT University, Vellore, India, and SSN College of Engineering, Chennai India, for funding the respective research labs where the research  ... 
doi:10.26555/ijain.v7i2.610 fatcat:g7t3j2bddrdhdokcj2jwtqxh7m

Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning

Ellen M. Ditria, Sebastian Lopez-Marcano, Michael Sievers, Eric L. Jinks, Christopher J. Brown, Rod M. Connolly
2020 Frontiers in Marine Science  
Deep learning methods provide a faster, cheaper, and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance and have much to offer the field of  ...  We produced three models using an object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata).  ...  Although recent advances in deep learning can make image analysis for animal ecology more efficient, there are still some ecological and environmental limitations.  ... 
doi:10.3389/fmars.2020.00429 fatcat:4bzybb4j3fcvzbsdtwddzewsci

Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms [article]

Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs
2016 arXiv   pre-print
Two primary goals are to develop transferable technologies for detection and classification in, one: the area of advanced algorithms, such as deep learning and other methods; and two: advanced systems,  ...  Overarching goals for this work aim to advance the state of the art for detection, classification and localization (DCL) in the field of bioacoustics.  ...  Yann LeCun's group at NYU, is focused on basic research and development for applying deep learning technologies to detect and classify underwater sounds in real-time.  ... 
arXiv:1605.00972v2 fatcat:pfpynxuubjg6jn3msiq3iqw3p4

Transferring deep knowledge for object recognition in Low-quality underwater videos

Xin Sun, Junyu Shi, Lipeng Liu, Junyu Dong, Claudia Plant, Xinhua Wang, Huiyu Zhou
2018 Neurocomputing  
Even with the insufficient training set, the transfer framework can well learn a recognition model for the special underwater object recognition task together with the help of data augmentation.  ...  In recent years, underwater video technologies allow us to explore the ocean in scientific and noninvasive ways, such as environmental monitoring, marine ecology studies, and fisheries management.  ...  Real time object recognition framework 3.1 Offline deep CNN model via transfer learning A key advantage of Deep Learning is its ability of learning stable and robust features from massive amounts of data  ... 
doi:10.1016/j.neucom.2017.09.044 fatcat:zety2uy5lbhhpd6sabfafyfrdi

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey [article]

Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi
2022 arXiv   pre-print
A promising method to address this problem is the cutting-edge Deep Learning (DL) technology.DL can help marine scientists parse large volumes of video promptly and efficiently, unlocking niche information  ...  Finally, we provide insights into the marine habitat monitoring research domain and shed light on what the future of DL for underwater image processing may hold.  ...  This facilitates monitoring underwater fish and can advance marine studies concerned with fish ecology.  ... 
arXiv:2203.06951v2 fatcat:oacfgeo5evanpatgnlq2hhne4y

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning [article]

Ellen Ditria, Sebastian Lopez-Marcano, Eric Jinks, Michael Sievers, Christopher J Brown, Rod Connolly
2019 bioRxiv   pre-print
Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance.  ...  We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata).  ...  , counting, and describing wild animals in camera-trap 515 images with deep learning.  ... 
doi:10.1101/805796 fatcat:bvvatkrusfgn3kg66yeyayfeka

Perspectives on individual animal identification from biology and computer vision

Maxime Vidal, Nathan Wolf, Beth Rosenberg, Bradley P Harris, Alexander Mathis
2021 Integrative and Comparative Biology  
Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications.  ...  We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.  ...  Two methods have been widely used for animal identification with deep learning: classification and metric learning.  ... 
doi:10.1093/icb/icab107 pmid:34050741 pmcid:PMC8490693 fatcat:zp32mrr56fcvda4ubc3r2xotja

Applications for deep learning in ecology [article]

Sylvain Christin, Eric Hervet, Nicolas Lecomte
2018 bioRxiv   pre-print
A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition.  ...  We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit.  ...  Phase 2: DCL system using deep learning approaches for land-based or ship-based real-time recognition and localization of marine mammals -machine learning detection algorithms.  ... 
doi:10.1101/334854 fatcat:f35v3u7z5zdjrjccu32uhqwfvu

A Literature Review of Underwater Image Detection [chapter]

Yawen Wang, Qinxiao Wang, Shasha Jin, Wei Long, Lingxi Hu
2022 Frontiers in Artificial Intelligence and Applications  
In this paper, first of all introduces the existing research of underwater target image recognition, mainly presents the underwater image recognition technology based on deep learning.  ...  Besides, the future development of underwater image recognition technology and its application in wisdom fishery is also discussed, which provides other scholar with some theoretical help and a comprehensive  ...  With the emergence of R-CNN, deep learning began to develop rapidly in object recognition and detection, and a group of scholars emerged to apply deep learning to underwater image recognition research,  ... 
doi:10.3233/faia220009 fatcat:ynod4ci7tvaitnwvgcmljfxix4

Phase 1: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - HPC System Implementation [article]

Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs
2016 arXiv   pre-print
The two primary goals are to develop transferable technologies for detection and classification in: (1) the area of advanced algorithms, such as deep learning and other methods; and (2) advanced systems  ...  We aim to investigate advancing the state of the art of detection, classification and localization (DCL) in the field of bioacoustics.  ...  NOPP -DCL Stellwagen Bank National Marine Sanctuary, development of pulse train detection, animal vs noise (see example in this report).  ... 
arXiv:1605.00971v2 fatcat:q2dbjwat6baqxhn632kh4lbpim
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