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Detection of Marine Litter on Arctic Beaches Based on Aerial Photography Using the YOLOv8s CNN
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Researchers used aerial photos and an AI computer program to spot marine litter on remote Arctic beaches in Russia. The program was best at identifying metal and plastic items, but had more trouble with wood and other debris that blended into the natural surroundings.
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.
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AI – Driven Marine Debris Detection for Ocean Conservation
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Researchers developed an AI-driven marine debris detection system using the YOLOv8 deep learning model trained to identify plastic waste in challenging underwater conditions including low visibility and complex backgrounds. The system aims to provide scalable, automated monitoring to support ocean conservation and guide debris removal efforts.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
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Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.
Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches
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Researchers surveyed marine litter on a remote Arabian Gulf island after a large cleanup, then trained a YOLO-v5 deep learning model on 10,400 beach images to automatically detect debris, achieving 90% detection accuracy and demonstrating that windward shores accumulate significantly more litter from neighboring countries.
Detection and Classification of Floating Waste on Water Surfaces Using YOLO-Based Algorithms
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Researchers trained AI models to spot floating trash—like plastic bottles, bags, and cans—in water using photos, with their best system (YOLOv11) correctly identifying waste about 83% of the time. This kind of technology could help clean-up crews find and remove plastic pollution before it breaks down into microplastics, which can end up in the fish and water we consume and have been linked to potential health concerns.
Marine Debris Recognition with Artificial Intelligence
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Millions of tons of plastic enter our oceans every year, breaking down into tiny microplastics that work their way up the food chain and onto our plates. Researchers built an AI tool that scans underwater photos and automatically spots trash with about 82% accuracy, which could help cleanup groups and governments find and target pollution hotspots faster than manual monitoring allows. This kind of tech won't solve ocean plastic pollution on its own, but it's a practical step toward tracking the problem that ultimately threatens seafood safety and human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.