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.
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