Highway　slope　disasters　show　obvious　stage　characteristics　before　the　occurrence,　and　cracks　are　the　early　symptoms　of　most　highway　slope　disasters.　Computer　vision　is　widely　used　in　crack　detection　because　of　its　advantages　of　high　efficiency　and　low　cost.　In　view　of　the　shortage　of　traditional　crack　actual　area　calculation　methods　and　poor　effect,　this　paper　proposes　a　slope　crack　pixel　level　segmentation　method　based　on　deep　convolutional　neural　network,　so　as　to　generate　accurate　segmentation　of　crack　morphology.　Then,　according　to　the　binary　segmentation　mask,　the　checkerboard　mapping　method　is　proposed　to　calculate　the　actual　crack　area.　Finally,　the　effectiveness　and　superiority　of　the　proposed　checkerboard　mapping　method　are　verified　and　evaluated　with　a　self-made　data　set　of　highway　crack　image.　The　experimental　results　show　that　this　method　can　effectively　detect　the　actual　crack　area,　and　the　relative　error　is　small.　The　calculated　results　can　be　used　as　a　reference　for　slope　disaster　warning.　©　2021,　ICST　Institute　for　Computer　Sciences,　Social　Informatics　and　Telecommunications　Engineering.