Frequency purification and interactive fusion for UAV-based VIS-IR object detection

Visible-infrared object detection for Unmanned Aerial Vehicles (UAV) can exploit the texture details of visible images and the thermal radiation information of infrared images, thereby improving target perception capability in complex environments. However, existing methods mainly focus on cross-modal feature interaction and feature fusion, while ignoring high-frequency interference in single-modal features before fusion, such as thermal noise, pseudo-hotspots, texture redundancy, and background edges. As a result, high-frequency noise is further amplified during cross-modal propagation, reducing detection accuracy. To address these problems, this paper proposes a UAV visible-infrared object detection method based on frequency purification and interactive fusion. Through a “purification-before-fusion” strategy, the proposed method suppresses single-modal high-frequency interference and enhances cross-modal complementary information. Specifically, a Low-frequency Aggregation Frequency Purification (LAFP) module is designed to use cross-modal low-frequency structural priors to guide the selection and purification of high-frequency detail features in each modality, suppressing invalid high-frequency responses while preserving target edges and local discriminative features. In addition, a Bidirectional Interactive Guidance Fusion (BIGF) module is introduced. This module employs bidirectional cross-attention to mine the complementary relationships between purified visible and infrared features, and combines a dynamic gating mechanism to achieve adaptive cross-modal feature fusion. Experimental results on three visible-infrared object detection datasets, namely DroneVehicle, VEDAI, and LLVIP, show that the proposed method achieves good performance in both complex aerial vehicle detection and low-light pedestrian detection scenarios. Compared with existing state-of-the-art methods, the proposed method achieves mean Average Precision (mAP) values of 82.4% and 80.5% on the DroneVehicle and VEDAI datasets, respectively, outperforming the second-best methods by 3.0% and 2.7%. On the LLVIP dataset, it obtains an average precision of 96.1%, which is 0.7% and 0.4% lower than the best and second-best results, respectively, but still demonstrates strong competitiveness. The experimental results demonstrate that the proposed algorithm has high effectiveness and robustness in UAV object detection scenarios.