MFE-Net: A Hybrid Perception and Manifold-Preserving Framework for Robust Fine-Grained Underwater Object Detection

High-performance underwater object detection constitutes a pivotal technological foundation for advancing marine intelligent computing and facilitating the autonomous deployment of subsea platforms. Nevertheless, underwater visual perception is inherently constrained by non-linear optical degradation, manifesting as an ill-posed inverse problem that induces ``semantic confusion'' and the ``feature annihilation'' of sub-pixel entities. Conventional deep learning architectures, predicated on strided downsampling, frequently function as irreversible low-pass filters, precipitating a catastrophic loss of information entropy regarding sparse high-frequency geometric manifolds during hierarchical transmission. To address these challenges, this study formulates MFE-Net, a hierarchical hybrid perception and manifold-preserving framework. The Hybrid Perception Feature Extraction (C3CF) module harmonizes local convolutional inductive biases with global attention recalibration to effectively suppress non-structured scattering noise and enhance deep feature representation. Building upon this, the Multi-Scale Feature Enhancement Neck (MFE-Neck) incorporates a lossless Fine-grained Feature Enhancement Branch and an Efficient Multi-Scale Feature Integration (EMFI) mechanism, leveraging Soft Nearest-neighbor Interpolation (SNI) to ensure signal fidelity and manifold consistency across heterogeneous scales. Extensive quantitative evaluations on specialized benchmarks (RUOD, DUO, and DeepFish) demonstrate that MFE-Net achieves 86.4% mAP_{50} and 63.9% mAP_{50-95} on RUOD with a balanced computational load of 12.8 GFLOPs, surpassing contemporary state-of-the-art (SOTA) architectures within the evaluated scope.