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This research aims to improve underwater target detection accuracy using an enhanced YOLO11 algorithm.

July 23, 2026Scientific ReportsOpen Access

Underwater biological target detection algorithm based on DVS-YOLO11

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Randomized trial demonstrates improved target detection accuracy in underwater environments, suggesting enhanced performance.

This research aims to improve underwater target detection accuracy using an enhanced YOLO11 algorithm.
Introduced a Dynamic Feature Fusion mechanism in the C3k2 module to enhance multi-scale feature representation.
Developed a Global Context mechanism to adaptively recalibrate features in the Visual Feature Enhancement module.
Improved the Shape-IoU loss function by integrating Normalized Wasserstein Distance for better bounding box regression.
DVS-YOLO11 achieved an mAP@0.5 of 84.78% and an mAP@0.5:0.95 of 64.67%.
Precision was recorded at 87.44% and Recall at 75.57%.
For scallop targets, mAP@0.5 improved from 63.9% to 68.6%, showing significant enhancements over baseline YOLO11.

Jia et al. (2026) studied this question.

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