Abstract:
Preservation of biodiversity is of utmost importance and one of the major ecosystem
which is highly in danger and a notable cause of disturbing biodiversity is freshwater
ecosystem. Marine researchers and scientists along with government bodies are developing
ways to monitor and preserve the underwater living organisms for a sustainable
environment. Many techniques have been developed for underwater monitoring and
classification but with advancement in knowledge the trends are changing rapidly. An
automated system for underwater fish monitoring and classification is needed but the
underwater environment is highly variable and complex which poses a great challenge
for such automated systems to accurately monitor and identify the inhabitants. We
proposed a variant of deep learning method namely Convolutional Neural Network
for specie identification in unconstrained underwater environment. We demonstrated
how the convolutional layers within a network can provide useful information if used
appropriately. The classification was done using dataset obtained from University of
Western Australia (UWA). We indicated many reasons that the proposed approach
can potentially be used for further classification tasks.
