CCCNet: Criss-cross attention enhanced cross layer refinement network for lane detection in complex scenarios

Abstract

Lane detection plays a crucial role in autonomous driving systems by enabling vehicles to comprehend road structure and ensure safe navigation. However, the current performance of lane line detection models, such as CCNet, exhibits limitations in handling difficult driving conditions like shadows, nighttime, no lines,and dazzle, which significantly impact the safety of autonomous driving. In addition, due to the lack of attention to both the global and local aspects of road images, this issue becomes even more pronounced. To address these challenges, we propose a novel network architecture named Criss-Cross Attention Enhanced Cross-Layer Refinement Network (CCCNet). By integrating the strengths of criss-cross attention and cross-layer refinement mechanisms, CCCNet effectively captures long-range dependencies and global context information from the input images, leading to more reliable lane detection in complex environments. Extensive evaluations on standard datasets, including CULane and TuSimple, demonstrate that CCCNet outperforms CLRNet and other leading models by achieving higher accuracy and robustness, especially in challenging scenarios. In addition, we publicly release our code and models to encourage further research advancements in lane detection technologies at https://github.com/grass2440/CCCNet.