Automatic Detection and Distribution Mapping of Epibenthos from Seabed Images

Abstract

The efficient investigation of fishery resources is critical for rapidly understanding the effects of abrupt environmental changes. Seabed imagery has been used extensively for resource assessment in scallop fisheries in the Sea of Okhotsk, Hokkaido, Japan. However, the potential of these images for the broader investigation of epibenthos remains unclear. In this paper, we propose an automatic detection method for epibenthos from seabed images using deep learning, specifically Mask R-CNN and Mask2Former models. We focus on four species: Asterias amurensis, Distolasterias nipon, Halocynthia aurantium, and Patiria pectinifera. The Mask R-CNN X101-FPN 3x model show the highest overall accuracy, with a mask mAP of 77.8%, whereas Mask2Former excelled in specific species detection. The trained models are successfully used to generate epibenthic distribution maps, demonstrating the effectiveness of the proposed method for monitoring large-scale marine ecosystems. This approach significantly enhances our ability to conduct comprehensive assessments of benthic communities, thereby providing an effective tool for marine-biodiversity assessment and fishery resource management.