With the rapid expansion of the low-altitude economy, detecting micro unmanned aerial vehicles (UAVs) in complex urban environments faces severe challenges. In optical surveillance, micro-UAVs typically appear as weak and small targets lacking distinct spatial semantics, rendering conventional appearance-based feature extraction highly ineffective. Furthermore, extreme lighting conditions and complex backgrounds easily submerge these subtle textures into noise. While event cameras can uniquely capture the high-frequency rotational characteristics of UAV rotors thanks to their microsecond temporal resolution, their sparse asynchronous outputs are highly susceptible to environmental clutter. To address this, we propose EGDNet, an event-guided frequency-aware dual-modal detection network optimized for robust UAV sensing. First, we developed a custom co-axial beam-splitting hardware platform to construct a strictly pixel-aligned dual-modal dataset of 43,090 frames, eliminating inter-modal parallax at the physical level. Second, our framework introduces a novel frequency-domain deconstruction pipeline utilizing a Temporal Fast Fourier Transform (T-FFT) to isolate the high-frequency mechanical rotor vibrations from low-frequency background clutter. Additionally, a Cross-Modal Synergistic Enhancement Module (CMSEM) and a Cross-Scale Adaptive Fusion Pyramid Network (CSAFPN) are designed to leverage event-derived high-frequency details for RGB feature restoration and to preserve sub-pixel target signatures during deep downsampling. Experimental results demonstrate that EGDNet achieves a mean average precision of 74.63% (AP@0.5) and an F1 score of 78.57%, significantly outperforming state-of-the-art fusion architectures. This work provides a robust visual perception solution for intelligent low-altitude surveillance.