SFE-Mamba: A Spatial–Frequency Collaborative Enhancement Network for Object Detection in Snowy Road Scenes

Abstract

Snowflake occlusion, reflections from accumulated snow, and low-contrast imaging in snowy environments can degrade target features and impair road-object detection performance. To address these challenges, this paper proposes SFE-Mamba, a snowy-weather object detection network built on Mamba-YOLO. First, a Snow Spatial–Frequency Feature Extraction Stem (SSF Stem) is designed to jointly model target features through dual spatial- and frequency-domain branches. The frequency-domain information is decomposed into low-, mid-, and high-frequency components and adaptively weighted, thereby preserving target structural information while suppressing frequency disturbances caused by snow particles, snow cover reflections, and illumination variations. Second, a Snow-Aware Mamba Attention Block (SMA Block) is developed by integrating local structural modeling, two-dimensional selective scanning, and Multi-Dimensional Perception Self-Attention (MPSA). This design strengthens the interaction between local details of weak targets and long-range contextual information, improving feature representation under occlusion and low-contrast conditions. Finally, Focus Convolution (FConv) is introduced to reduce the loss of target edges, textures, and positional information during scale transformation. Experiments were conducted on the real-world snowy dataset Snow-ACDC and the simulated snowy dataset Snow-VOC. SFE-Mamba achieved mAP50 values of 74.2% and 71.8% on the two datasets, respectively, outperforming the Mamba-YOLO baseline by 3.6 and 3.9 percentage points. Compared with CF-YOLO, a detector specifically designed for snowy scenes, SFE-Mamba further improved the mAP50 by 1.6 and 1.8 percentage points on the two datasets, respectively. The model contains 6.8 M parameters and reaches an inference speed of 58 FPS. The experimental results demonstrate that the proposed method effectively improves object detection accuracy in complex snowy road scenes while maintaining a favorable balance between detection performance and computational efficiency.