Machine　vision　enables　machines　to　extract　rich　information　from　image　or　video　data　and　make　intelligent　decisions.　However,　approaches　using　artificial　synapse　hardware　systems　significantly　limit　the　real-time　and　accuracy　in　machine　vision　segmentation　amid　complex　environments.　Addressing　this,　we　propose　a　novel　three-terminal　adaptive　artificial-light-emitting　synapse　(AALS)　capable　of　photoelectric　double　output　along　with　adaptive　behavior.　The　device　uses　silver　nanowires　(AgNWs)　as　polar　conductive　bridges　to　reduce　reliance　on　transparent　electrodes,　while　polyvinyl　alcohol　(PVA)　dielectric　layers　adaptively　modulate　charge　carrier　concentrations　in　conductive　channels.　Additionally,　we　have　designed　an　adaptive　parallel　neural　network　(APNN)　and　applied　it　to　autonomous　driving　image　processing.　This　innovation　significantly　reduces　adaptation　time　and　notably　enhances　mean　pixel　accuracy　(MPA)　for　semantic　segmentation　under　overexposure　and　low-light　conditions　by　142.2%　and　304.4%,　respectively.　Therefore,　this　work　introduces　new　strategies　for　advanced　adaptive　vision,　promising　significant　potential　in　intelligent　driving　and　neuromorphic　computing.