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MFPD: Mamba-Driven Feature Pyramid Decoding for Underwater Object Detection
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DOI：10.1109/LSP.2025.3639347.png)
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摘要
En 中文
Underwater object detection suffers from limited long-range dependency modeling, fine-grained feature representation, and noise suppression, resulting in blurred boundaries, frequent missed detections, and reduced robustness. To address these challenges, we propose the Mamba-Driven Feature Pyramid Decoding framework, which employs a parallel Feature Pyramid Network and Path Aggregation Network collaborative pathway to enhance semantic and geometric features. A lightweight Mamba Block models long-range dependencies, while an Adaptive Sparse Self-Attention module highlights discriminative targets and suppresses noise. Together, these components improve feature representation and robustness. Experiments on two publicly available underwater datasets demonstrate that MFPD significantly outperforms existing methods, validating its effectiveness in complex underwater environments.
Keyword:
Feature extraction
Object detection
Adaptation models
Robustness
Computational modeling
Noise reduction
Noise
Interference
Decoding
Background noise
Underwater object detection
Mamba-Driven
collaborative pathway
期刊
I
IF:
3.9
论文数:
784
被引数:
0
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SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
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