These　past　six　years　have　witnessed　that　segmentation　algorithms　make　a　breakthrough　due　to　the　development　of　deep-based　semantic　segmentation　networks,　which　can　realize　classification　from　image-level　to　pixel-level.　However,　when　segmenting　a　small-batch,　complex-features　and　obvious-gap　database,　these　semantic　segmentation　networks　will　lead　to　segmentation　ambiguities　in　some　boundaries　between　foreground　(i.e.　the　main　segmentation　body)　and　its　background　with　limited　image　detail　expression.　To　solve　such　problem,　this　paper　proposes　an　optimized　segmentation　scheme　for　ambiguous　pixels　based　on　Atrous-ResFCN　and　MFR-DenseNet,　namely　ARMD.　Firstly,　masking　algorithm　and　adaptive　box　mechanism　are　applied　in　pre-processed　KITTI　segmentation　database　to　construct　the　classification　database,　which　will　be　used　to　train　ambiguities　judgment　model.　In　addition,　Atrous-ResFCN-8s　and　Atrous-ResFCN-16s　are　proposed　and　further　combined　to　determine　segmentation　ambiguities　with　different-level　segmentation　abilities.　Finally,　MFR-DenseNet　is　further　migrated　to　optimize　these　ambiguous　pixels　with　effective　threshold　selection.　Experimental　results　demonstrate　that　our　proposed　ARMD　algorithm　is　beneficial　to　improve　segmentation　accuracy,　where　the　highest　MIoU　and　PA　are　able　to　reach　88.18%　and　95.88%,　respectively.