DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection
This study addresses the challenge of balancing domain generalization and accuracy in fine-grained traffic object detection across cross-city scenarios by proposing the DRAFE framework. The method employs a heterogeneous asymmetric fusion mechanism integrating LW-DETR and RF-DETR, combined with two-stage training to construct high-quality corpora. During inference, complementary hypotheses are effectively recovered and transfer robustness is enhanced through anchor-conditioned matching, reliability-weighted fusion, and protocol-aware confidence recalibration. Validated on Track 6 of the AI City Challenge 2026, DRAFE achieved a mAP of 0.4022, ranking sixth and outperforming the baseline ensemble by 0.0553 mAP, thereby demonstrating its effectiveness in real-world cross-domain traffic detection tasks.