A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images

Fish detection and species classification in underwater environments using deep learning with temporal information

(2020) Ahsan Jalal et al.   Ecological Informatics

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

(2020) Ellen M. Ditria et al.   Frontiers in Marine Science

StoX: An open source software for marine survey analyses

(2019) Espen Johnsen et al.   Methods in Ecology and Evolution

Automatic segmentation of fish using deep learning with application to fish size measurement

(2019) Rafael Garcia et al.   ICES JOURNAL OF MARINE SCIENCE

From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass

(2018) Roland Proud et al.   ICES JOURNAL OF MARINE SCIENCE

OUP accepted manuscript

(2018)   ICES JOURNAL OF MARINE SCIENCE

Fish identification from videos captured in uncontrolled underwater environments

(2016) Faisal Shafait et al.   ICES JOURNAL OF MARINE SCIENCE

Quantifying changes in abundance, biomass, and spatial distribution of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic seas from 2007 to 2014

(2015) Leif Nøttestad et al.   ICES JOURNAL OF MARINE SCIENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep Vision: An In-Trawl Stereo Camera Makes a Step Forward in Monitoring the Pelagic Community

(2014) Melanie J. Underwood et al.   PLoS One

Large mesopelagic fishes biomass and trophic efficiency in the open ocean

(2014) Xabier Irigoien et al.   Nature Communications

DeepVision in-trawl imaging: Sampling the water column in four dimensions

(2013) Shale Rosen et al.   FISHERIES RESEARCH

Development and application of an empirical multifrequency method for backscatter classification

(2010) Alex De Robertis et al.   CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES