Lane　detection　is　an　indispensable　technology　for　environmental　perception　and　is　a　function　of　autonomous　vehicles.　Although　many　researchers　have　applied　deep　learning　to　lane　detection　and　achieved　good　results,　their　application　scenarios　were　relatively　simple.　When　a　lane　is　blocked,　lost,　or　met　with　other　challenges,　the　accuracy　of　lane　detection　decreases　tremendously.　This　paper　proposes　a　novel　semantic　segmentation　network　for　lane　detection　that　includes　IBN-Net,　an　attention　module,　and　an　encoder-decoder　structure　called　IAED.　IBN-Net　improves　the　modelling　and　generalisation　capability　with　little　computational　complexity.　The　attention　module　can　better　capture　context　information　and　improve　the　performance　of　the　network.　We　evaluated　the　performance　of　the　proposed　method,　which　improved　by　6.3%　over　ResNet34　on　the　TuSimple　datasets.　Tests　on　the　CULane　datasets　showed　that　our　method　is　at　least　3%　better　than　existing　methods.　Experimental　results　showed　that　the　proposed　method　has　strong　robustness　under　complex　conditions　such　as　insufficient　illumination　and　shadow　occlusion.