ECPNet: An Enhanced Curve Perception Network for Lane Detection

Abstract

Lane detection methods based on anchors have received increasing attention, but fixed-shape anchors make it difficult to model complex lane line shapes. To solve this problem, we propose an Enhanced Curve Perception Network (ECPNet). Specifically, we propose a Layer-by-layer Context Fusion (LCF) module to fully utilize both high-level and low-level features in lane detection by establishing short hop connections across feature layers of diverse scales. Then, we propose a novel Structural Correction Prediction (SCP) module, which enhances the detection ability of the model on the curve structure lane by dynamically guiding the selection of anchor classification patterns. In addition, ECPNet adaptive calibration pays attention to channel features through the Cross-Channel Attention (CCA) mechanism. Experiments on the two most representative datasets demonstrate that the proposed method achieves state-of-the-art performance in a variety of environments, especially in curvy lanes.