Semantic　line　is　a　high-level　feature　in　image　segmentation　and　is　of　great　significance　in　image　understanding.　This　paper　proposes　a　semantic　line　detection　framework　shorten　as　SLDF　in　addressing　robot　guidance　or　navigation　tasks.　First　of　all,　this　paper　cast　semantic　line　detection　task　as　a　special　object　detection　task　with　oriented　bounding　boxes,　and　then　discusses　the　diversity　between　several　kinds　of　label　coding　categories.　There　are　two　alternative　backbones　provided　by　SLDF:　A　two-stage　model　in　pursuit　of　higher　precision,　or　a　single　stage　model　that　reaches　the　best　trade-off　between　speed　and　accuracy.　Besides,　a　geometry-based　loss　function　is　designed　for　SLDF　to　exploit　the　benefits　of　localization　accuracy　and　brings　significant　improvement.　Finally,　considering　the　lack　of　a　comprehensive　line　evaluation　standard　including　angle,　offset,　length　and　other　factors,　this　paper　respectively　designs　corresponding　evaluation　function　based　on　the　characteristics　of　frame-independent,　frame-dependent　and　streaming　perception　model,　and　then　evaluate　the　performance　of　our　framework　on　SEL-Dataset,　NKL-Dataset.　Finally,　we　migrate　the　designed　model　to　the　agricultural　robot　and　realize　the　robot　guidance　task　in　the　farmland.