Lane detection is a critical task in autonomous driving, which requires accurately predicting the complex topology of lanes in various scenarios. While previous methods of lane detection have shown success, challenges still exist, especially in scenarios where lane markings are absent. In this paper, we analyze the role of global and local features in accurately detecting lanes and propose a Hybrid Global-Local Perception Network (HGLNet) to leverage them. Global and local features play distinct roles in lane detection by respectively aiding in the detection of lane instances and the localization of corresponding lanes. HGLNet extracts global semantic context by utilizing a global extraction head that aggregates information about adaptive sampling points around lanes, achieving an optimal trade-off between performance and efficiency. Moreover, we introduce a Multi-hierarchy feature aggregator (MFA) to capture feature hierarchies in both regional and local ranges, elevating the representation of local features. The proposed Hybrid architecture can simultaneously focus on global and local features at different depth levels and efficiently integrate them to sense the global presence of lanes and accurately regress their locations. Experimental results demonstrate that our proposed method improves detection accuracy in various challenging scenarios, outperforming the state-of-the-art lane detection methods.