December 5, 2025Remote SensingOpen Access

Diffusion-Enhanced Underwater Debris Detection via Improved YOLOv12n Framework

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Authors

JTJianghan TaoWaseda University FZFan Zhao YCYijia Chen

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Implication

The UDD-YOLO framework improves image contrast for underwater debris detection, achieving 81.8% mAP and suggesting advancements in environmental monitoring.

Key Points

Image contrast enhancement improves underwater debris detection effectiveness, achieving a mAP of 81.8%.
The UDD-YOLO framework employs a Cold Diffusion module and AMC2f feature extraction to increase detection precision.
Assessment on a public underwater pollution dataset includes 15 debris categories to verify model effectiveness.
Findings suggest diffusion-driven enhancements can significantly improve marine perception systems for ecological monitoring.

Cite This Study

Tao et al. (2025) studied this question.

synapsesocial.com/papers/693231368e51979591dcea4d https://doi.org/10.3390/rs17233910

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Implication

The UDD-YOLO framework improves image contrast for underwater debris detection, achieving 81.8% mAP and suggesting advancements in environmental monitoring.

Diffusion-Enhanced Underwater Debris Detection via Improved YOLOv12n Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

Diffusion-Enhanced Underwater Debris Detection via Improved YOLOv12n Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

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