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March 3, 2026
Detecting marine pollution made simple with YOLOv12: A multi-class approach for real-time debris recognition
YL
Yang Li
LG
Li Guangxue
FX
Feng Xiuli
Key Points
Real-time detection of marine pollution effectively identifies various debris types, aiding cleanup efforts.
Accuracy metrics indicate up to 92% precision in recognizing different plastics and organic waste.
This analysis employs a computer vision framework, utilizing the YOLOv12 architecture for efficient real-time processing.
The findings highlight the potential for using advanced recognition algorithms to enhance environmental management efforts.
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Li et al. (Tue,) studied this question.
synapsesocial.com/papers/69a75ab3c6e9836116a20dcd
https://doi.org/https://doi.org/10.1016/j.rsma.2026.104801
Detecting marine pollution made simple with YOLOv12: A multi-class approach for real-time debris recognition | Synapse