Multi-Fish Tracking in Complex Underwater Environments Using BF-DETR and DAPS-Enhanced BoT-SORT

Reliable multi-object tracking of fish is essential for individual-level behavior analysis and intelligent aquaculture monitoring. However, degraded underwater imagery, ambiguous boundaries in dense groups, and high inter-individual appearance similarity often lead to missed detections, association errors, and identity switches. We propose BF-DETR-DAPS-BoT-SORT, a tracking-by-detection framework integrating fish detection, identity representation, and trajectory association. BF-DETR incorporates a Fish Morphology Enhancement Module (FMEM) to model local texture, directional structure, and contextual information, and a Boundary-Contrast Separation Module (BCSM) to distinguish adjacent individuals. We further develop the Detail-Aware Part Similarity Network (DAPS), which combines underwater texture enhancement, dual-axis local representations, and part-similarity learning to capture subtle appearance differences under changes in swimming direction. DAPS features are integrated into BoT-SORT for data association. On task-specific datasets constructed from continuous underwater videos, BF-DETR achieved mAP@0.5 and mAP@0.5:0.95 values of 96.63% and 77.53%, respectively. The complete framework achieved MOTA, HOTA, and IDF1 scores of 93.1%, 75.8%, and 91.2%, respectively, with 13 identity switches. Compared with BoT-SORT, HOTA and IDF1 increased by 1.4 and 2.1 percentage points, while identity switches decreased from 18 to 13. These results indicate improved trajectory continuity and identity consistency in complex underwater scenes, supporting reliable individual-level fish behavior analysis.