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Assessing fish abundance from underwater video using deep neural networks

https://doi.org/10.1109/ijcnn.2018.8489482
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/lom3.10113openalex-snapshot
Fish species classification in unconstrained underwater environments based on deep learning
inferredcites →https://doi.org/10.1007/978-3-319-10578-9_23openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_26openalex-snapshot
Edge Boxes: Locating Object Proposals from Edges
inferredcites →https://doi.org/10.1007/s11042-015-2601-xopenalex-snapshot
Fine-grained object recognition in underwater visual data
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot
The Pascal Visual Object Classes Challenge: A Retrospective
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.biocon.2017.03.027openalex-snapshot
Enhancing the performance of marine reserves in estuaries: Just add water
inferredcites →https://doi.org/10.1016/j.ecss.2017.10.003openalex-snapshot
Umbrellas can work under water: Using threatened species as indicator and management surrogates can improve coastal conservation
inferredcites →https://doi.org/10.1071/mf13230openalex-snapshot
Use of underwater video to assess freshwater fish populations in dense submersed aquatic vegetation
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot
Fast R-CNN
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1145/2647868.2654889openalex-snapshot
Caffe
inferredcites →https://doi.org/10.3354/meps12115openalex-snapshot
Habitat type and beach exposure shape fish assemblages in the surf zones of ocean beaches
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection
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A video monitoring and computational system for estimating migratory juvenile fish abundance in river systems
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Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network
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GSDCN: A Customized Two-Stage Neural Network for Benthonic Organism Detection
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Out of the shadows: automatic fish detection from acoustic cameras
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Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish
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Multi-stream fish detection in unconstrained underwater videos by the fusion of two convolutional neural network detectors
inferred← citeshttps://doi.org/10.1007/s10489-024-06224-0openalex-snapshot
A data-centric framework for combating domain shift in underwater object detection with image enhancement
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Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review
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A systematic review and analysis of deep learning-based underwater object detection
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Pomerania Fish: A dataset for fishes across Pomerania freshwater waterbodies in-situ environments
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Fast and Accurate Fish Classification from Underwater Video using You Only Look Once
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DePondFi’23 Challenge on Real-Time Pond Environment: Methods and Results
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A Marine Benthic Detection Algoritm Based On Improved Lightweight Network
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Vision Technology in Underwater: Applications, Challenges and Perspectives
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Semi-automatic Approach to Create Fish Image Datasets for Aquaculture Applications
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