针对水下鱼群检测的物理退化问题，提出轻量高效的新模型。
FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture
- 分频解耦双流结构，分离处理不同频率信息损失。
- 在自建数据集上达到92.8% mAP，参数减少29%。
- 适合复杂浑浊水域的实时智能养殖监控场景。
水下鱼群检测是智慧水产和海洋生态监测的核心能力。现有检测器通过堆叠特征提取器或引入重型注意力模块提升精度，但计算开销大，且忽视根本限制因素：波长相关的吸收与浑浊引起的散射会显著降低对比度、模糊细结构并引入后向散射噪声，导致定位与识别不可靠。为此，我们提出FinSight-Net，一种面向复杂养殖环境的高效且物理感知的检测框架。该模型采用多尺度解耦双流处理（MS-DDSP）瓶颈，通过异构卷积分支显式应对频率特异性信息丢失，抑制后向散射伪影，同时通过尺度感知与通道加权路径补偿失真的生物线索。进一步设计高效路径聚合特征金字塔网络（EPA-FPN），通过长程跳跃连接与冗余融合路径剪枝，恢复深层网络中常被衰减的高频空间信息，实现对非刚性鱼体在严重模糊与浑浊条件下的鲁棒检测。在DeepFish、AquaFishSet及自建挑战性数据集UW-BlurredFish上的实验表明，FinSight-Net达到当前最优性能。尤其在UW-BlurredFish上，mAP达92.8%，较YOLOv11s提升4.8%，参数量减少29.0%，为智慧水产中的实时自动化监测提供了强大且轻量的解决方案。
原文摘要 · Abstract (English)
Underwater fish detection (UFD) is a core capability for smart aquaculture and marine ecological monitoring. While recent detectors improve accuracy by stacking feature extractors or introducing heavy attention modules, they often incur substantial computational overhead and, more importantly, neglect the physics that fundamentally limits UFD: wavelength-dependent absorption and turbidity-induced scattering significantly degrade contrast, blur fine structures, and introduce backscattering noise, leading to unreliable localization and recognition. To address these challenges, we propose FinSight-Net, an efficient and physics-aware detection framework tailored for complex aquaculture environments. FinSight-Net introduces a Multi-Scale Decoupled Dual-Stream Processing (MS-DDSP) bottleneck that explicitly targets frequency-specific information loss via heterogeneous convolutional branches, suppressing backscattering artifacts while compensating distorted biological cues through scale-aware and channel-weighted pathways. We further design an Efficient Path Aggregation FPN (EPA-FPN) as a detail-filling mechanism: it restores high-frequency spatial information typically attenuated in deep layers by establishing long-range skip connections and pruning redundant fusion routes, enabling robust detection of non-rigid fish targets under severe blur and turbidity. Extensive experiments on DeepFish, AquaFishSet, and our challenging UW-BlurredFish benchmark demonstrate that FinSight-Net achieves state-of-the-art performance. In particular, on UW-BlurredFish, FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%, providing a strong and lightweight solution for real-time automated monitoring in smart aquaculture.
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