Density-Response-Oriented Low-Visibility and High-Density Underwater Fish Counting for Intelligent Recirculating Aquaculture Monitoring

Non-contact underwater fish counting supports stocking-density estimation and management in intelligent aquaculture. This study evaluated a 900-image annotated TV5/IOC in-domain dataset derived from the right-view stream of one source video. For each of three seed-controlled splits, 720 images were used for training and 180 for validation. All controlled configurations used the same raw single-view (Raw-SV) supervised and inference input. High-density-aware density modeling (HDA-DM) applies a mild sample weight only to the density loss of high-count training images. HDA-DM reduced the Main900 three-seed mean MAE from 4.024 ± 0.578 to 3.739 ± 0.414 (7.07%) and reduced the Hard200 challenging-case mean MAE from 9.799 ± 1.792 to 8.863 ± 0.304 (9.56%). The improvement was not identical in every seed. Cross-view density distillation (CVDD), evaluated as a training-only ablation, did not further improve HDA-DM: the combined configuration obtained 3.987 ± 0.635 on Main900 and 9.666 ± 1.373 on Hard200. A loss-scope audit further showed that the original density-only HDA-DM formulation outperformed weighting density, count, and shape supervision together. HDA-DM introduces no additional inference-stage parameters or operations. Because the evidence is limited to three seeds and one acquisition source, all comparisons are descriptive and no formal statistical-significance or external-generalization claim is made.