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Plastic Waste Over the Ocean: An Approach to Surface Recognition
Summary
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.