Cross-View Visible-Thermal Object-Level Change Detection for Small Vehicles

Object-level change detection is a critical task in remote sensing with broad applications in urban traffic monitoring and smart city management. However, existing methods are primarily validated on aligned single-modal datasets, failing to adapt to real-world challenges such as cross-view misalignment and multimodal feature discrepancy. To address these, we propose a Visible-Thermal Cross-View Object Change Detection (CVOCD) Dataset and a corresponding Cross-Modal Cross-View Object Change Detection (CMOCD) framework. The CMOCD framework integrates two core modules: the Coarse-to-Fine Feature Alignment (CFFA) module resolves spatial misalignment by first establishing global coarse correspondence and then aligning pixel-level fine consistency. The Visible-Thermal Feature Fusion (VTFF) module leverages multimodal complementarity through decoupled channel-spatial attention, enhancing feature discriminability in various environments. Comparative results show that CMOCD achieves a mAP@0.5 of 56.5% on the CVOCD dataset, outperforming six state-of-the-art single-modal detectors. The dataset and code will be published athttps://github.com/CAI42/CMOCD.