Benchmarking UAV-based Vehicle Re-Identification under Simulated Weather Conditions

2026-07-12

AI Summary

This study addresses the insufficient robustness of existing drone-based vehicle re-identification methods under adverse weather conditions and the lack of systematic evaluation. To this end, we introduce, for the first time, a physics-based fog and rain synthesis mechanism within a unified framework, establishing an analytical pipeline for simulating weather effects. We conduct consistent robustness evaluations of CLIP-ReID, MSINet, and AdaSP on the VRU and UAV-VeID benchmarks. Experimental results demonstrate that rainfall degrades performance significantly more than fog. Among the evaluated methods, AdaSP achieves the best robustness, attaining mAP scores of 93.0% and 88.5% under fog and rain on VRU-Large, and 88.7% and 76.2% on UAV-VeID-Test, respectively. These findings systematically reveal the differential impact of distinct weather conditions on vehicle re-identification performance.

Abstract

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.

Problem

• UAV-based vehicle Re-Identification
• adverse weather
• robustness
• aerial imagery
• weather degradation

Innovation

• UAV-based vehicle ReID
• adverse weather simulation
• robustness benchmarking
• synthetic weather effects
• aerial surveillance