To reduce computational costs and efficiently complete lane detection tasks, this paper proposes a multiframe lane detection method using a Swin Transformer embedded with a coordinate attention mechanism for lane detection in continuous multiframe image sequences. In this approach, continuous multiframe image sequences are taken as inputs and the Swin Transformer encoder-decoder architecture is adopted to ensure consistent input and output image sizes. The coordinate attention mechanism is embedded in patch merging from the stage 3 fusion layer of the Swin Transformer model, enhancing the model's focus on long-distance dependencies and its ability to extract both global and local features of lane lines. Additionally, introducing spatiotemporal long-short term memory between the encoder and decoder boosts the model's ability to predict temporal sequence information, significantly improving the lane line detection accuracy. Extensive experiments conducts on the CULane, Tusimple, and VIL-100 datasets demonstrate that the proposed method provides a comprehensive advantage in handling continuous multiframe image sequences, delivering superior detection performance compared to existing studies.