Aiming at the problems of insufficient local detail capture ability and high computational complexity of the LSTR algorithm in complex road scenarios, this paper proposes a dynamic multi-path covariance Transformer detection model DMCTR. Firstly, a feature boosting and suppression module is constructed. Through feature enhancement and suppression operations, the problem of missed detection of weak features such as curved lanes and dashed line segments in traditional convolution is alleviated. Secondly, construct a dynamic enhancement dual-path aggregation block, utilize deformable convolution to adapt to lane geometric deformation, and combine the dual-attention mechanism to enhance local geometric features. Finally, the cross-covariance attention is introduced into the Transformer architecture to replace the multi-head self-attention in the Transformer encoder. The experimental results show that on the TuSimple dataset, the accuracy rate of the method proposed reaches 96.74%, which is 0.56% higher than that of the baseline LSTR model. In the complex scenarios of the CULane dataset, the F1 has increased by 4.48%, and the detection accuracy in special scenarios such as blurred lane lines and strong light at night has been significantly improved. This enables the model to maintain real-time performance (353 fps) while effectively solving the feature modeling bottleneck of traditional methods in complex scenarios.