FSDJL-Net v1

Vehicle detection under adverse weather conditions often suffers from low contrast, blur, and sparse textures, accompanied by noise and scattering interference, which makes it difficult for detectors to stably capture critical structural cues. Existing methods usually emphasize either frequency-domain or spatial-domain cues, or directly fuse them at shallow stages, which may introduce noise and fail to fully exploit the complementarity between the two domains. To this end, we propose FSDJL-Net, a Frequency-Spatial Domain Joint Learning network for vehicle detection. Specifically, we design a Joint Domain Feature Downsampling (JDFD) module to suppress noise during downsampling while constructing complementary representations; propose a Frequency Domain Feature Extraction (FDFE) module to strengthen the modeling of high-frequency cues (e.g., details and edges) while maintaining inference efficiency; and further build a Joint Domain Learning (JDL) module, which performs large-receptive-field spatial modeling and enables more effective cross-domain interaction and fusion to obtain more robust joint representations.恶劣天气条件下的车辆检测任务常面临低对比度、图像模糊与纹理稀疏等问题，同时伴随噪声与散射干扰，导致检测器难以稳定捕捉关键结构线索。现有方法往往仅侧重频域或空域线索，或直接在浅层阶段对二者进行融合，这可能引入噪声且无法充分挖掘两类域间的互补性。为此，我们提出FSDJL-Net：一种面向车辆检测的频域空域联合学习网络。具体而言，我们设计了联合域特征下采样（Joint Domain Feature Downsampling, JDFD）模块，在特征下采样过程中抑制噪声并构建互补表征；提出频域特征提取（Frequency Domain Feature Extraction, FDFE）模块，在保障推理效率的同时，强化对细节、边缘等高频线索的建模；进一步构建联合域学习（Joint Domain Learning, JDL）模块，该模块可实现大感受野空域建模，并支持更高效的跨域交互与融合，从而获得鲁棒性更强的联合表征。