An　automatic　monitoring　method　for　highway　slope　cracks　was　proposed,　aiming　to　timely　detect　crack　problems　and　reduce　potential　hazards.　Taking　highway　slope　cracks　as　the　research　object,　for　the　characteristics　of　irregularity　of　crack　image　pattern　and　large　interference　of　surrounding　environment,　a　highway　slope　cracks　segmentation　network　(SCSNet)　and　a　highway　slope　crack　geometric　parameter　calculation　method　were　designed.　An　encoder　was　used　to　gradually　capture　higher-level　semantic　features,　and　a　decoder　was　used　to　fuse　information　between　different　scales　by　gradually　recovering　spatial　information　and　combining　jump　connections.　Then,　for　the　case　of　complex　road　slope　crack　images　and　other　situations,　a　channel　attention　mechanism　was　used　to　learn　the　features　between　channels　and　enhance　the　feature　representation　of　cracks.　A　method　based　on　the　connectivity　domain　analysis　was　proposed　to　obtain　the　crack　connectivity　domain　and　calculate　the　crack　length,　width　and　area　geometric　parameters.　Results　show　that　the　average　intersection　ratio　of　the　segmentation　network　reaches　87.　86%,　which　can　extract　the　highway　slope　crack　features　better,　and　the　current　state　of　the　cracks　can　be　measured　more　accurately　using　the　crack　geometric　parameter　calculation　method.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.