Lane　detection　is　challenging　under　varied　light　conditions　(e.g.,　night,　shadow,　and　dazzling　light)　because　a　lane　becomes　blurred　and　extracting　features　becomes　more　difficult.　Some　researchers　have　proposed　methods　based　on　multitask　learning　and　contextual　information　to　solve　this　problem;　however,　these　methods　result　in　additional　computing.　A　data　enhancement　method　based　on　retinex　theory　is　proposed.　This　method　improves　the　adaptability　of　a　lane　model　under　varied　light　conditions.　In　particular,　we　design　an　image　enhancement　network　for　calculating　the　reflectivity　of　images,　modifying　their　exposure,　and　then　generating　images　with　consistent　exposure.　These　images　are　fed　to　the　lane　detection　model　for　training　and　detection.　Our　network　consists　of　two　parts:　exposure-consistent　image　generation　and　lane　detection.　We　validate　our　method　on　the　CULane　dataset,　and　results　show　that　it　can　improve　lane　detection　performance,　particularly　on　light-related　datasets.　(C)　2022　SPIE　and　IS&T