Auxiliary Edge Traction Network for Vehicle Re-Identification Based on Multimodal Aerial Images

Due to their outstanding flexibility and safety, unmanned aerial vehicles (UAVs) have received widespread attention in the fields of video surveillance and target tracking. However, conventional RGB cameras may not meet the needs of dark environments and harsh scenes. The Synthetic Aperture Radar (SAR) camera, being independent of optical imaging, enables observations under a variety of weather conditions and at extended ranges. Currently, there is no complete cross-modal vehicle re-identification dataset in the field of remote sensing. We have collected and constructed a dataset named Unmanned Aerial Vehicle Multimodal Vehicle Re-identification (UAV-Mu), which includes 11 identities, 2598 RGB samples, 2621 infrared samples, and 770 SAR samples. In addition, to address the significant differences between modalities, we propose an Auxiliary Edge Traction Network (AET-Net). AET-Nat can effectively aggregate different layers of the network, thereby enhancing attention to vehicle outline details to output more discriminative features. Comprehensive experimental results demonstrate that the proposed AET-Net outperforms several other advanced methods.