DenseFish-v13: A Symmetry-Aware NMS-Free YOLOv13-Mamba Framework for Dense Underwater Fish Detection and Bio-Kinematic Behavior Recognition

Dense underwater aquaculture poses significant challenges for intelligent image processing because asymmetric occlusion, turbidity, aeration-like bubbles, and motion blur frequently degrade fish contours and quasi-periodic scale textures. These disturbances often cause conventional detectors to miss detections, merge bounding boxes, experience feature collapse, and exhibit unstable counting. To address this problem, we propose DenseFish-v13, a symmetry-aware NMS-free YOLOv13-Mamba framework for dense underwater fish detection and bio-kinematic behavior recognition. The framework integrates a Bio-Harmonic Frequency Gate to preserve biological texture patterns while suppressing bubble-like frequency noise, a Bi-directional Multi-scale Wavelet Mamba backbone for global occlusion-aware structure recovery, and an asymmetry-aware density repulsion strategy to separate highly overlapping fish instances during bipartite matching. In addition, a lightweight Bio-Kinematic Behavior Head converts continuous detections into interpretable trajectory descriptors for behavior-state recognition. Experiments on the Dense-Aqua benchmark, constructed from public aquaculture datasets, show that DenseFish-v13 achieves 64.8% mAP@50:95 and a Counting MAE of 3.7 on the overall test set, while reaching 64.2% mAP@50:95 and a Counting MAE of 4.1 on the extreme-density split. Under a strong synthetic bubble perturbation, the model shows only a 1.3 percentage-point drop in mAP and maintains 125 FPS on Jetson Orin NX. These results demonstrate its effectiveness in robust, real-time underwater aquaculture monitoring.