A quantitative detection algorithm based on improved faster R-CNN for marine benthos-Reference-Cited by-同舟云学术

A quantitative detection algorithm based on improved faster R-CNN for marine benthos

Published:2021-03 Issue: Volume:61 Page:101228
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Liu Yong,Wang Shengnan

Funder

Qingdao University of Science and Technology

Institute of Oceanology, Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modelling,Modelling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference36 articles.

1. Real-time robust detector for underwater live crabs based on deep learning;Cao;Comput. Electron. Agric.,2020

2. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images;Cheng;IEEE Trans. Geosci. Remote Sens.,2016

3. The Pascal visual object classes (VOC) challenge;Everingham;Int. J. Comput. Vis.,2010

4. Underwater cable detection in the images using edge classification based on texture information;Fatan;Measurement,2016

5. Fast R-CNN;Girshick,2015

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MarineYOLO: Innovative deep learning method for small target detection in underwater environments;Alexandria Engineering Journal;2024-10

2. An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion;Fishes;2024-08-27

3. Analysis of recent techniques in marine object detection: a review;Multimedia Tools and Applications;2024-07-11

4. Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification;Ecological Informatics;2024-07

5. Marine zoobenthos recognition algorithm based on improved lightweight YOLOv5;Ecological Informatics;2024-05