FFCANet: a frequency channel fusion coordinate attention mechanism network for lane detection

Lane line detection becomes a challenging task in complex and dynamic driving scenarios. Addressing the limitations of existing lane line detection algorithms, which struggle to balance accuracy and efficiency in complex and changing traffic scenarios, a frequency channel fusion coordinate attention mechanism network (FFCANet) for lane detection is proposed. A residual neural network (ResNet) is used as a feature extraction backbone network. We propose a feature enhancement method with a frequency channel fusion coordinate attention mechanism (FFCA) that captures feature information from different spatial orientations and then uses multiple frequency components to extract detail and texture features of lane lines. A row-anchor-based prediction and classification method treats lane line detection as a problem of selecting lane marking anchors within row-oriented cells predefined by global features, which greatly improves the detection speed and can handle visionless driving scenarios. Additionally, an efficient channel attention (ECA) module is integrated into the auxiliary segmentation branch to capture dynamic dependencies between channels, further enhancing feature extraction capabilities. The performance of the model is evaluated on two publicly available datasets, TuSimple and CULane. Simulation results demonstrate that the average processing time per image frame is 5.0 ms, with an accuracy of 96.09% on the TuSimple dataset and an F1 score of 72.8% on the CULane dataset. The model exhibits excellent robustness in detecting complex scenes while effectively balancing detection accuracy and speed. The source code is available at https://github.com/lsj1012/FFCANet/tree/master