As a core technology in the visual navigation systems of intelligent vehicles, lane detection directly influences path planning and steering control, playing a crucial role in improving both traffic safety and navigation efficiency. However, lane images are often dominated by background information, particularly in long-range scenarios where lane exhibit weak features, missing markings, or occlusions. These issues, compounded by perspective-induced width variations, make long-range lane significantly more difficult to detect than nearby ones.To address these challenges, this paper proposes a lane detection method that incorporates enhanced spatial perception. First, considering the elongated structural characteristics of lane in images, strip pooling is introduced into the backbone network to refine the representation of lane features. Second, we design an Enhanced Spatial-Aware Optimizer (ESAO) and a Lane Multi-Scale Aggregator (LMSA). The ESAO enhances small-scale feature representations and effectively distinguishes targets from background information. The LMSA is a lane-shape-aware multi-scale aggregation module that strengthens the local feature representation and correlation at each anchor point. It enables effective fusion of global and local features and reconstructs the complete lane shape by incorporating prior knowledge of lane geometry.In addition, a global and local slope consistency loss function is introduced to adaptively refine the shape and position of lanes.Experimental results demonstrate that the proposed method outperforms the best baseline approaches, achieving improvements of 0.58% in F1-score and 0.44% in accuracy on the TuSimple dataset, and a 1.14% increase in F1@50-score on the CULane dataset. Notably, the method exhibits more stable performance in long-distance road scenarios.
