Single-Domain Generalized Object Detection (SDGOD) aims to achieve robust detection on unseen domains using only a single labeled source domain. Existing methods typically apply image- or feature-level perturbations independently, limiting their ability to model diverse domain shifts. To address this, we propose Fourier-based Dual-level Perturbation (FDP), which injects Gaussian perturbations into the amplitude spectra of both images and features in the Fourier domain. FDP consists of three modules: a Pseudo Domain Diversity (PDD) module that generates diverse pseudo-domains through spatial-frequency transformations, enriching image-level diversity. a Domain-Specific Spectral Perturbation (DSSP) module that introduces learnable low-frequency perturbations in feature space. and a Consistency Loss (CL) that encourages domain-invariant representation learning across perturbed views. Comparative experiments with ten mainstream SDGOD algorithms demonstrate that FDP achieves superior performance in single-domain generalized object detection tasks.