Wavelet-guided Misalignment-aware Network for Visible-Infrared Object Detection

• URL: http://arxiv.org/abs/2507.20146v1
• Date: Sun, 27 Jul 2025 06:53:31 GMT
• Title: Wavelet-guided Misalignment-aware Network for Visible-Infrared Object Detection
• Authors: Haote Zhang, Lipeng Gu, Wuzhou Quan, Fu Lee Wang, Honghui Fan, Jiali Tang, Dingkun Zhu, Haoran Xie, Xiaoping Zhang, Mingqiang Wei,
• Abstract summary: We propose the Wavelet-guided Misalignment-aware Network (WMNet) to adaptively address different cross-modal misalignment patterns.<n>By jointly exploiting low and high-frequency information, WMNet alleviates the adverse effects of noise, illumination variation, and spatial misalignment.<n>It enhances the representation of salient target features while suppressing spurious or misleading information, thereby promoting more accurate and robust detection.
• Score: 21.634585158312763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared object detection aims to enhance the detection robustness by exploiting the complementary information of visible and infrared image pairs. However, its performance is often limited by frequent misalignments caused by resolution disparities, spatial displacements, and modality inconsistencies. To address this issue, we propose the Wavelet-guided Misalignment-aware Network (WMNet), a unified framework designed to adaptively address different cross-modal misalignment patterns. WMNet incorporates wavelet-based multi-frequency analysis and modality-aware fusion mechanisms to improve the alignment and integration of cross-modal features. By jointly exploiting low and high-frequency information and introducing adaptive guidance across modalities, WMNet alleviates the adverse effects of noise, illumination variation, and spatial misalignment. Furthermore, it enhances the representation of salient target features while suppressing spurious or misleading information, thereby promoting more accurate and robust detection. Extensive evaluations on the DVTOD, DroneVehicle, and M3FD datasets demonstrate that WMNet achieves state-of-the-art performance on misaligned cross-modal object detection tasks, confirming its effectiveness and practical applicability.

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