Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system von Salman, Ahmad, Siddiqui, Shoaib Ahmad, Shafait, Faisal, Mian, Ajmal, Shortis, Mark R, Khurshid, Khawar, Ulges, Adrian, Schwanecke, Ulrich

Volltext

Automatic segmentation of fish using deep learning with application to fish size measurement von Garcia, Rafael, Prados, Ricard, Quintana, Josep, Tempelaar, Alexander, Gracias, Nuno, Rosen, Shale, Vagstol, Havard, Lovall, Kristoffer

Volltext

Image-based, unsupervised estimation of fish size from commercial landings using deep learning von Álvarez-Ellacuría, Amaya, Palmer, Miquel, Catalán, Ignacio A, Lisani, Jose-Luis

Volltext

Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks von Tseng, Chi-Hsuan, Kuo, Yan-Fu

Volltext

How emerging data technologies can increase trust and transparency in fisheries von Probst, Wolfgang Nikolaus

Volltext

Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards von French, Geoff, Mackiewicz, Michal, Fisher, Mark, Holah, Helen, Kilburn, Rachel, Campbell, Neil, Needle, Coby

Volltext

Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning von Li, Qiong, Sun, Xin, Dong, Junyu, Song, Shuqun, Zhang, Tongtong, Liu, Dan, Zhang, Han, Han, Shuai

Volltext

Improving the classification accuracy of fishes and invertebrates using residual convolutional neural networks von Zhou, Z, Yang, X, Ji, H, Zhu, Z

Volltext

Recent advances of machine vision technology in fish classification von Li, Daoliang, Wang, Qi, Li, Xin, Niu, Meilin, Wang, He, Liu, Chunhong

Volltext

Deep learning for detection and counting of Nephrops norvegicus from underwater videos von Burguera, Antoni Burguera, Bonin-Font, Francisco, Chatzievangelou, Damianos, Fernandez, Maria Vigo, Aguzzi, Jacopo

Volltext

Identifying the species of harvested tuna and billfish using deep convolutional neural networks von Lu, Yi-Chin, Tung, Chen, Kuo, Yan-Fu

Volltext

An in-situ image enhancement method for the detection of marine organisms by remotely operated vehicles von Ouyang, Wenjia, Wei, Yanhui, Hou, Tongtong, Liu, Junnan

Volltext

Contrastive learning-based image retrieval for automatic recognition of in situ marine plankton images von Yang, Zhenyu, Li, Jianping, Chen, Tao, Pu, Yuchun, Feng, Zhenghui

Volltext

Automatic detection of Western rock lobster using synthetic data von Mahmood, Ammar, Bennamoun, Mohammed, An, Senjian, Sohel, Ferdous, Boussaid, Farid, Hovey, Renae, Kendrick, Gary

Volltext

Evaluating automated benthic fish detection under variable conditions von Scoulding, Ben, Maguire, Kylie, Orenstein, Eric C

Volltext

Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition von Paoletti, Giancarlo, Beyan, Cigdem, Del Bue, Alessio

Volltext

The Prince William Sound Plankton Camera: a profiling in situ observatory of plankton and particulates von Campbell, R. W., Roberts, P. L., Jaffe, J.

Volltext

Semi-supervised target classification in multi-frequency echosounder data von Choi, Changkyu, Kampffmeyer, Michael, Handegard, Nils Olav, Salberg, Arnt-Børre, Brautaset, Olav, Eikvil, Line, Jenssen, Robert

Volltext

Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish von Eickholt, Jesse, Kelly, Dylan, Bryan, Janine, Miehls, Scott, Zielinski, Dan

Volltext

Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishing von Qiao, Maoying, Wang, Dadong, Tuck, Geoffrey N, Little, L Richard, Punt, Andre E, Gerner, Mike

Volltext