Robust Lane Detection Based on Informative Feature Pyramid Network in Complex Scenarios

Lane detection plays a fundamental role in autonomous driving systems, yet it remains challenging under complex real-world conditions such as low illumination, occlusion, and degraded lane markings. In this paper, we propose a novel lane detection framework, Informative Feature Pyramid Network (Info-FPNet), designed to improve multi-scale feature representation and alignment for robust lane detection. Specifically, the proposed architecture integrates two key modules: an informative feature pyramid (IFP) module and a cross-layer refinement (CLR) module. The IFP module selectively aggregates spatially and semantically informative features across different scales using pixel shuffle upsampling, feature alignment, and semantic encoding mechanisms, thereby preserving fine-grained details and minimizing aliasing effects. The CLR module applies region-wise attention and anchor regression to refine coarse lane proposals, enabling better localization of curved or occluded lanes. Experimental results on two public benchmarks, CULane and TuSimple, demonstrate that the proposed Info-FPNet outperforms state-of-the-art approaches in terms of F1 score and is robust under challenging conditions such as nighttime, strong reflections, and occlusions. Furthermore, the proposed method maintains real-time inference speed and low computational overhead, validating its effectiveness and practicality in real-world applications.