Multiresolution Context Augmentation and Dual-Channel Attention for 3-D Lane Detection

Qirui Ning, Jinlai Zhang, Yuhang Xie, Kaifeng Liu, Kai Gao, Bin Chen, Gengbiao Chen, Qing Fan, Hui Liu, Ronghua Du

Abstract: Three-dimensional lane detection is a fundamental yet highly challenging task in autonomous driving, as the presence of interference and blurring in images often impedes accurate detection. To address these challenges, we propose the MRDALane framework, which introduces two novel modules: the dual-channel attention module (DCAM) and the multiresolution context augmentation (MRCA). The DCAM utilizes a dual-channel attention mechanism to effectively suppress noise and emphasize salient features, significantly enhancing lane detail capture in complex environments. The MRCA incorporates multiple dilated convolutions with a sawtooth dilation rate design, enabling diverse feature learning across branches and improving robustness across different road conditions. Experimental results demonstrate that MRDALane outperforms state-of-the-art (SOTA) 3-D lane detection models, such as LATR and CurveFormer, on both the Apollo and OpenLane datasets. Notably, in robustness experiments under adverse conditions, MRDALane achieved an $F1$ score improvement of up to 13.66% compared to the LATR model. This comprehensive performance evaluation demonstrates our model’s superior detection accuracy across various challenging scenarios. These advancements provide new insights for future research in 3-D lane detection, contributing to the ongoing development of autonomous driving technology and road safety. Our code will be released at https://github.com/Dcelysia/MRDALane.git

External IDs:doi:10.1109/jiot.2025.3614228