DAFF-Net: A Dual-Branch Attention-Guided Feature Fusion Network for Vehicle Re-Identification

Abstract

Vehicle re-identification (Re-ID) is a critical task in the fields of intelligent transportation and urban surveillance. This task faces numerous challenges, such as significant changes in shooting angles, strong similarities in appearance between different vehicles of the same model, and difficulties in modeling fine-grained differences. To overcome the shortcomings of existing methods in local feature extraction and multi-scale fusion, this paper proposes an attention-guided dual-branch feature fusion network (DAFF-Net). The network uses ResNet50-ibn as its backbone and designs two complementary feature extraction branches. One branch fuses cross-layer attention between shallow and deep features, introducing a Temperature-Calibration Attention Fusion Module (TCAF) to improve the accuracy of cross-layer feature fusion effectively. The other branch enhances multi-scale attention for mid-layer features, constructing a Multi-Scale Gated Attention Module (MSGA) to extract local details and directional structural information. Finally, the discriminative ability of the enhanced features is improved by concatenating the two branch features and jointly optimizing the network using triplet loss, cross-entropy loss, and center loss. Experimental results on the VeRi-776 and VehicleID public datasets indicate that the proposed DAFF-Net outperforms existing mainstream methods in multiple key metrics. On the VeRi-776 dataset, mAP and CMC@1 increased to 82.2% and 97.5%, respectively. In the three test subsets of the VehicleID dataset, the CMC@1 metric achieved 90.7%, 84.6%, and 82.1%, respectively, demonstrating the effectiveness of the proposed network in vehicle re-identification tasks.