What does this research mean for the field?

The YOLO v11n-DDH model improves detection precision and recall for spatial targets in river and lake environments, achieving a mean Average Precision (mAP) of 83.9%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to enhance target recognition in river and lake spaces, addressing challenges such as varying scales and reflections.

February 19, 2026SensorsOpen Access

Multi-Objective Detection of River and Lake Spaces Based on YOLOv11n

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Authors

LLLing LiuTianjin Agricultural University TSTianyue SunTianjin Agricultural University XGXiaoying GuoTianjin Agricultural University

Discussion

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Implication

Demonstrates improved detection of aquatic targets using a new model in river and lake settings, indicating enhanced environmental monitoring capabilities.

Key Points

The aim is to enhance target recognition in river and lake spaces, addressing challenges such as varying scales and reflections.
Developed YOLO v11n-DDH model incorporating DySnakeConv for feature extraction.
Integrated DAttention for noise suppression and key feature enhancement.
Utilized HSFPN for multi-level feature fusion to improve semantic representation.
Conducted experiments on a self-constructed dataset to assess effectiveness.
Achieved precision of 88.4%, recall of 78.9%, and mAP of 83.9%.
Improved mAP by 3.4 percentage points compared to the original YOLO model.
DySnakeConv contributed a 0.6 percentage point increase in mAP@50.
DAttention and HSFPN improved mAP@50 by 0.3 and 0.9 percentage points, respectively.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6996a7c3ecb39a600b3edc11 https://doi.org/10.3390/s26041274

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February 19, 2026SensorsOpen Access

Multi-Objective Detection of River and Lake Spaces Based on YOLOv11n

View Full Paper

Ask AI

Bookmark

Authors

LLLing LiuTianjin Agricultural University TSTianyue SunTianjin Agricultural University XGXiaoying GuoTianjin Agricultural University

Discussion

Member takes

Implication

Demonstrates improved detection of aquatic targets using a new model in river and lake settings, indicating enhanced environmental monitoring capabilities.

Key Points

The aim is to enhance target recognition in river and lake spaces, addressing challenges such as varying scales and reflections.
Developed YOLO v11n-DDH model incorporating DySnakeConv for feature extraction.
Integrated DAttention for noise suppression and key feature enhancement.
Utilized HSFPN for multi-level feature fusion to improve semantic representation.
Conducted experiments on a self-constructed dataset to assess effectiveness.
Achieved precision of 88.4%, recall of 78.9%, and mAP of 83.9%.
Improved mAP by 3.4 percentage points compared to the original YOLO model.
DySnakeConv contributed a 0.6 percentage point increase in mAP@50.
DAttention and HSFPN improved mAP@50 by 0.3 and 0.9 percentage points, respectively.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6996a7c3ecb39a600b3edc11 https://doi.org/10.3390/s26041274

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