FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture

TL;DR

FinSight-Net tackles underwater fish detection under challenging optical constraints by introducing physics-aware MS-DDSP bottlenecks and EPA-FPN detail restoration. The approach decouples frequency-domain degradations and reconstitutes high-frequency spatial cues, enabling robust, real-time detection on edge platforms. Across DeepFish, AquaFishSet, and UW-BlurredFish, it achieves state-of-the-art $mAP_{50}$ (e.g., 92.8% on UW-BlurredFish) with substantially fewer parameters, demonstrating strong generalization to turbid and non-rigid aquatic targets. This work provides a practical, efficient solution for smart aquaculture monitoring and marine ecological sensing.

Abstract

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.

Figures (8)

• Figure 1: Each method’s overall performance(’P’) on test data and generalization performance (’G’) on unseen data are linked by dashed lines. Our detector demonstrates a clear advantage over all SOTA methods in both metrics.
• Figure 2: Architectural topology and physics-aware paradigm of FinSight-Net. (Top) Overall pipeline integrating CSPDarknet with EPA-FPN (Module A) and MS-DDSP (Module B) for robust feature extraction. (Bottom) Conceptual framework mapping Jaffe-McGlamery optical constraints to recovery streams: High-Frequency Recovery suppresses backscattering ($I_{bs}$), while Spectral Restoration compensates for wavelength-dependent absorption ($e^{-\eta d}$).
• Figure 3: MS-DDSP Bottleneck. This module employs parallel heterogeneous branches to decouple and compensate for underwater optical degradations , utilizing soft attention to optimize SNR and restore biological structural fidelity.
• Figure 4: EPA-FPN Architecture. This detail-filling mechanism utilizes vertical long-range skip connections to inject high-resolution spatial cues into deep semantic nodes, salvaging critical high-frequency structural information.
• Figure 5: Qualitative comparison of detection results. Compared to the YOLOv11s baseline, FinSight-Net significantly reduces missed detections and yields higher confidence scores in turbid and densely occluded aquaculture scenarios.