In order to solve the problems of inadequate feature fusion, low detection accuracy and poor robustness in current lane detection, this paper proposes a lane detection model called fusion of multi-branch structure and attention mechanism network (FMANet). In the image coding part, fusion of multi-branch structure and attention mechanism is adopted. swish is selected as the activation function, and the image decoding part adopts the jump connection structure to achieve cross-layer feature fusion. In this paper, TuSimple public dataset was used to evaluate and verify the FMANet model. The experimental results show that the mAP index of the FMANet model proposed in this paper is close to 97. 25%, and the lane detection accuracy reaches 98. 15%. In addition, CULane dataset verifies that the FMANet model has better robustness in different scenarios.