Objective Lane line detection as one of the core functions of autonomous driving systems is crucial for ensuring the safe driving of vehicles. However current segmentation-based detection methods show a significant imbalance between speed and performance which is particularly prominent in nighttime scenarios. To address this challenge this paper proposes a nighttime lane line detection model called DecoLaneNet. Methods First an efficient feature extraction module was designed. In this module a dual-branch decomposed convolution residual structure with different-sized receptive fields was constructed using decomposed convolutions which significantly reduced the number of model parameters while ensuring performance as much as possible and more accurately extracted lane line features. Then channel rearrangement technology and dilated convolutions were used to make up for performance losses and increase the receptive field. Finally a dual-branch upsampling module was introduced for feature decoding which significantly improved the model?? s segmentation accuracy. Results Extensive evaluations were conducted on the nighttime scenario dataset Night and the lane detection dataset CULane containing various traffic scenarios. The F1 score of DecoLaneNet reached 74. 7% on the Night dataset and 71. 5% on the CULane dataset. Remarkably despite having only 1. 94 M parameters the model maintained a speed of 62. 5 FPS on the Jetson development board. Conclusion The experimental results show that DecoLaneNet not only performs excellently in nighttime scenarios but also shows good performance in dealing with other complex scenarios. Moreover when the model is deployed on embedded devices its performance and efficiency are still better than those of other models showing excellent potential and feasibility.