Frequency-Domain-Based Multispectral Pedestrian Detection Network

In numerous all-weather applications, multi-modal pedestrian detection has been widely adopted, notably in autonomous driving and video surveillance. Exploiting the complementary strengths of different modalities is one of the most effective ways to enhance detection performance. However, misalignment in the spatial domain and variability in modality reliability can hinder robust cross-modal feature extraction, thereby limiting the potential of multi-modal pedestrian detection.To address these issues, this paper proposes a cross-modal object detection method based on unregistered optical and infrared images, termed the Frequency-Domain-Based Multispectral Pedestrian Detection Network (FD-MPedNet). Specifically, we design a Multi-Scale Channel-Spatial (MSCS) module that leverages deformable convolutions and multi-scale convolution operations to effectively capture edge information and extract features across different spatial scales, while preserving channel priors to avoid disregarding their inherent feature distributions. Additionally, we introduce an Adaptive Frequency Domain Feature Fusion (AFDFF) module, wherein the frequency bands of different modalities are decomposed to fuse low-frequency components for global information exchange, while retaining the original high-frequency features to enhance the model’ s sensitivity to object pixels. Experimental results on the challenging KAIST dataset demonstrate that the proposed FD-MPedNet achieves outstanding performance.