Small-Vehicle Change Detection in UAV Imagery via Physics-Aware Spatiotemporal Cues and Reproducible Evaluation

Abstract

Vehicle detection holds significant research value and practical application potential. However, existing vehicle detection algorithms struggle to meet the demands for fast, accurate detection and tracking of weak and small targets in uncrewed aerial vehicle (UAV) remote sensing imagery. In this work, weak targets refer to vehicle targets with small spatial extent, low contrast with the background, and subtle temporal changes, which are difficult to detect reliably in UAV imagery. To address this issue, this article proposes a bitemporal change detection method based on spatiotemporal features. Specifically, the proposed method involves processing the data captured by UAVs, applying feature point matching for image alignment, and performing change detection on bitemporal data to achieve more precise recognition of vehicle targets. The results demonstrate that the proposed algorithm exhibits more competitive detection performance compared to traditional unsupervised methods, such as change vector analysis, differential component analysis, iteratively weighted multivariate extinction detection, and multivariate detection. Compared to traditional methods, our proposed method achieves superior performance in detecting small and weak targets, particularly excelling in identifying weak targets while reducing the occurrence of false positives.