发表机构
UIT（UIT）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在模拟天气条件下基于无人机的车辆重识别，通过对CLIP-ReID、MSINet和AdaSP三种方法在VRU和UAV-VeID基准测试，发现恶劣天气降检索性能，AdaSP鲁棒性最强，凸显天气感知模型设计及评估协议的必要。
AI 中文摘要
基于无人机的车辆重识别因无人机提供的灵活视角和广域覆盖，在交通监控等应用中成为有前景的技术。然而，现有方法在恶劣天气下的鲁棒性研究不足。本文对三种代表性车辆重识别方法在两个基于无人机的基准上进行对比研究。通过天气影响管道生成合成雾天和雨天变体数据集，所有方法在匹配的清洁、雾天和雨天条件下训练和评估。实验表明恶劣天气会降低检索性能，雨造成的下降比雾大。AdaSP表现出最强鲁棒性。研究结果表明模拟恶劣天气增加了基于无人机的车辆重识别难度，揭示了方法间鲁棒性差异，凸显了未来空中重识别研究中天气感知模型设计和评估协议的必要性。代码已发布。
英文摘要
UAV-based vehicle re-identification (ReID) has emerged as a promising technique for traffic surveillance, urban monitoring, and public-safety applications thanks to the flexible viewpoints and wide-area coverage provided by unmanned aerial vehicles. However, despite recent progress on UAV-based vehicle ReID benchmarks, the robustness of existing methods under adverse weather remains insufficiently studied. This is important because weather degradation can significantly affect the fine-grained appearance cues required for reliable vehicle matching in aerial imagery, especially under small object scale, viewpoint variation, and complex backgrounds. In this paper, we present a controlled comparative study of three representative recent vehicle ReID methods, namely CLIP-ReID, MSINet, and AdaSP, on two UAV-based benchmarks, VRU and UAV-VeID. To ensure consistent robustness evaluation, we generate synthetic foggy and rainy variants of both datasets using an analytical weather-effect pipeline while preserving the original identities and data splits. All methods are then trained and evaluated under matched clean, foggy, and rainy conditions. Experimental results show that adverse weather consistently degrades retrieval performance across both datasets, with rain causing larger drops than fog in nearly all settings. Among the evaluated methods, AdaSP demonstrates the strongest robustness, achieving 93.0% and 88.5% mAP on VRU-Large, and 88.7% and 76.2% mAP on UAV-VeID-Test under foggy and rainy conditions, respectively. Overall, our findings show that simulated adverse weather substantially increases the difficulty of UAV-based vehicle ReID, reveals clear robustness differences among recent methods, and highlights the need for weather-aware model design and evaluation protocols in future aerial ReID research. The code is released at https://github.com/tranminhvu945/Benchmarking-ReID.
CommentsAccepted at the 2026 International Conference on Multimedia Analysis and Pattern Recognition (MAPR 2026)
