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Plastic Waste Over the Ocean: An Approach to Surface Recognition
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This study develops computer vision and remote sensing methods to detect and classify plastic waste floating on ocean surfaces. By training surface recognition algorithms on aerial and satellite imagery, the approach enables large-scale monitoring of marine plastic pollution. The work contributes to improved tracking of plastic debris distribution and could support targeted ocean cleanup efforts.
To address the challenges of low detection accuracy and slow recognition of floating objects on water surfaces, an enhanced YOLOv5 algorithm has been developed. This improved algorithm incorporates a coordinate attention mechanism to enhance spatial recognition and employs the GIOU loss function with updated anchor matching for quicker decision-making. The new method achieved a 97.39% mAP, marking a 15.28% improvement over the original YOLOv5, demonstrating its effectiveness.
More Papers Like This
Underwater Waste Recognition and Localization Based on Improved YOLOv5
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Researchers developed an improved YOLOv5-based algorithm incorporating weighted image fusion to enhance detection and localization of underwater plastic waste in optical images, addressing challenges of noise, low contrast, and blurred textures in aquatic environments.
Deep Learning Approaches for Ocean Waste Detection: A Comparative Study of YOLOv8 and Faster R-CNN
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Deep learning models (YOLOv8 and Faster R-CNN) were compared for detecting ocean waste, with Faster R-CNN achieving better precision for meso- and macroplastic detection, though both systems struggled with microplastics due to their tiny size and visual similarity to natural marine particles. This highlights the urgent need for specialized AI detection methods capable of identifying microplastics in ocean monitoring applications.
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.
YOLO-Aqua: Intelligent Detection of Plastic Debris in Aquatic Environments
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This study proposed an automated system combining YOLOv8 object detection with Particle Swarm Optimization (PSO) to detect plastic debris in underwater environments in real time. The system outperformed traditional YOLO models in accuracy, precision, and recall, and can be deployed on autonomous underwater vehicles for ocean cleanup and large-scale marine plastic monitoring.
AAYOLO: Robust Detection of Small and Occluded Floating Objects in Water Bodies Under Varying Illumination Using the FloW Dataset
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Researchers developed AAYOLO, an enhanced object detection model integrating atrous convolutions and channel attention mechanisms, achieving higher precision and recall than baseline YOLOv5s for detecting small, occluded, and poorly illuminated floating plastic debris under varying lighting conditions.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.