Detection of Marine Litter on Arctic Beaches Based on Aerial Photography Using the YOLOv8s CNN

Marine litter beach accumulation is a pressing issue not only for urban coastal areas but also for remote and uninhabited regions such as the Arctic. Existing manual litter collection and identification methods remain labor-intensive. An assessment of the volume and composition of marine litter was conducted for the Russian Arctic coastlines using high-resolution airphotos. The method is based on training the You Only Look Once (YOLOv8, 2023) convolutional neural network (CNN) in the Oriented Bounding Box (OBB) configuration to recognize plastic, metal, fishing gear, wood, and other types of marine litter on images. The best detection performance was obtained for metal and plastic—large, visually contrasting litter items. The worst performance was obtained for wood and other debris, primarily due to their similarity to the natural background. Machine learning (ML) methods combined with remote sensing data represent a new high-precision tool for large-scale environmental monitoring in the Arctic.