Target detection networks based on deep learning has some problems in the field of lane line recognition， such as unclear lane differences， low recognition accuracy， a high false detection rate， and a high missed detection rate. To solve the aforementioned problems， a lightweight lane detection and tracking network， SCNNLane， based on spatial instance segmentation， was proposed. In the coding part， the VGG16 network and the spatial convolution neural network （SCNN） were applied to improve the ability of the network structure to learn spatial relationships， which solved the problems of blurring and discontinuity in lane prediction. Simultaneously， based on LaneNet， two branch tasks after encoding the output were coupled to improve poor foreground and background recognition and indistinguishability between lanes. Finally， the method was compared with five other semantic segmentation-based lane-line algorithms by using the TuSimple dataset. Experimental results show that the accuracy of this algorithm is 97.12%， and the false detection rate and missed detection rate are reduced by 44.87% and 12.7% respectivel， as compared with LaneNet， thus meeting the demand of real-time lane line detection.