Xiang Liu

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.

Ultraviolet (UV) insect traps enable continuous field pest monitoring, but their images contain severe scale variation, dense and overlapping targets, incomplete specimens, background stains, insect fragments, non-target insects, inter-class similarity, and long-tailed distributions, leading to missed detections, false positives, and class confusion. To address these challenges, we constructed a real-world UV insect-trap pest dataset comprising 12,426 high-resolution images and 38,003 annotated instances from 36 pest categories, and propose FCD-DETR, a foreground-aware and context-enhanced Detection Transformer based on RT-DETR-R18. The main distinction of FCD-DETR lies in a progressive enhancement design tailored to UV insect-trap images, where FADM first reduces foreground–background confusion by decoupling pest foreground cues from multi-scale semantic information, C2f_CFBlock strengthens local-detail and contextual representations in the lightweight backbone, and DHSA further alleviates high-level semantic ambiguity through dynamic-range histogram self-attention in the Transformer encoder. On the proposed dataset, FCD-DETR achieved 64.42% mAP@0.5 and 41.33% mAP@50:95, improving the baseline by 5.25 and 5.11 percentage points, respectively, and outperforming representative CNN- and Transformer-based detectors. Comprehensive experimental analyses confirm that FCD-DETR improves foreground discrimination and detection robustness in complex UV insect-trap scenarios.