Treffer: IMPROVING DETECTION AND LOCALIZATION OF GREEN SEA URCHIN BY ADDING ATTENTION MECHANISMS IN A CONVOLUTIONAL NETWORK.

Green sea urchin, Strongylocentrotus droebachiensis, exerts considerable influence on the structure and function of marine benthic habitats in Arctic and sub-Arctic regions, including highly biodiverse kelp forests. The species' gonads (roe) also is a highly prized delicacy on Asian markets. Autonomous detection and quantification of the species' biomass and ecological footprint are desirable to address questions at relevant management scales. Underwater robots, equipped with proper object recognition algorithms, may be used for this purpose. The present study represents the first step in a research program on the development of a model for the recognition of green sea urchin in natural habitats. Several factors affect the detection of target objects in underwater environments (in the present case, green sea urchins), including lighting, background, and size and shape of the objects. We try to overcome some of these challenges by developing a multi-step process that includes colour enhancement and augmentation for accurate recognition of green sea urchins in natural habitats. We also proposed an improved model for better interpretation of target characteristics based on the state-of-the-art YOLOv7 object detector. To investigate the possible advantages on extracting the feature information of target objects, this study utilizes the spatial and channel attention mechanism. [ABSTRACT FROM AUTHOR]

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