Abstract:
:Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network. The neural network is trained on an imagery with 11 manually annotated litter categories and then evaluated on an independent part of the dataset, attaining a mean average precision score of 62%. The presence of other background features in the imagery (e.g., algae, seagrass, scattered boulders) resulted to higher number of predicted litter items compare to the observed ones. The results of the study are encouraging and suggest that deep learning has the potential to become a significant tool for automatically recognizing seafloor litter in surveys, accomplishing continuous and precise litter monitoring.
journal_name
Mar Pollut Bulljournal_title
Marine pollution bulletinauthors
Politikos DV,Fakiris E,Davvetas A,Klampanos IA,Papatheodorou Gdoi
10.1016/j.marpolbul.2021.111974subject
Has Abstractpub_date
2021-01-20 00:00:00pages
111974eissn
0025-326Xissn
1879-3363pii
S0025-326X(21)00008-4journal_volume
164pub_type
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