Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the performance of detectors in complex environments. However, existing methods often neglect the frequency characteristics of complementary information, such as the abundant high-frequency details in visible images and the valuable low-frequency thermal information in infrared images, thus constraining detection performance. To solve this problem, we introduce a novel Frequency-Driven Feature Decomposition Network for IVOD, called FD2-Net, which effectively captures the unique frequency representations of complementary information across multimodal visual spaces. Specifically, we propose a feature decomposition encoder, wherein the high-frequency unit (HFU) utilizes discrete cosine transform to capture representative high-frequency features, while the low-frequency unit (LFU) employs dynamic receptive fields to model the multi-scale context of diverse objects. Next, we adopt a parameter-free complementary strengths strategy to enhance multimodal features through seamless inter-frequency recoupling. Furthermore, we innovatively design a multimodal reconstruction mechanism that recovers image details lost during feature extraction, further leveraging the complementary information from infrared and visible images to enhance overall representational capacity. Extensive experiments demonstrate that FD2-Net outperforms state-of-the-art (SOTA) models across various IVOD benchmarks, i.e. LLVIP (96.2% mAP), FLIR (82.9% mAP), and M3FD (83.5% mAP).

翻译：红外-可见光目标检测旨在利用红外与可见光图像中的互补信息，从而提升检测器在复杂环境下的性能。然而，现有方法往往忽略了互补信息的频率特性，例如可见光图像中丰富的高频细节与红外图像中有价值的低频热信息，这限制了检测性能的提升。为解决此问题，我们提出了一种新颖的频率驱动特征分解网络（简称FD2-Net），该网络能够有效捕获跨模态视觉空间中互补信息特有的频率表征。具体而言，我们设计了一个特征分解编码器，其中高频单元利用离散余弦变换来捕获具有代表性的高频特征，而低频单元则采用动态感受野来建模不同目标的多尺度上下文信息。随后，我们采用一种无参数的互补优势策略，通过无缝的跨频率再耦合来增强多模态特征。此外，我们创新性地设计了一种多模态重建机制，以恢复特征提取过程中丢失的图像细节，进一步利用红外与可见光图像的互补信息来提升整体表征能力。大量实验表明，FD2-Net在多个红外-可见光目标检测基准数据集上均优于现有最优模型，即在LLVIP（96.2% mAP）、FLIR（82.9% mAP）和M3FD（83.5% mAP）上取得了领先性能。