SiamDC: Efficient UAV Visual Tracking via Collaborative Dual-Channel Enhancement and Cascaded Cross-Correlation Fusion

UAV visual tracking remains challenging because aerial imagery frequently contains small targets, visually similar distractors, camera motion, occlusion, and rapid appearance variation. To improve target representation and template–search matching under these conditions, we propose SiamDC, an anchor-free Siamese tracker built upon SiamCAR. SiamDC introduces a Dual-channel Collaborative Enhancement (DCE) module that jointly models spatial dependencies and inter-channel relationships within the template and search branches and further transfers branch-specific channel relationships reciprocally between them. In addition, a Cross-Correlation Feature Fusion (CFF) module is developed to perform a cascaded matching process: pixel-wise correlation first preserves fine-grained spatial correspondence, after which the correlation responses are fused with the search representation and further processed by channel-preserving depth-wise cross-correlation. Experiments on DTB70, UAV123, and UAV20L show consistent improvements over the SiamCAR baseline and competitive performance against the evaluated trackers while retaining real-time tracking capability. Under the standardized efficiency evaluation protocol, SiamDC requires 55.80 M parameters and 26.90 GFLOPs and achieves a network-forward speed of 44.1 FPS on an NVIDIA RTX 3080.