Tunnel　cracks　are　thin　and　narrow　linear　targets,　and　their　pixel　proportions　in　images　are　usually　very　low,　less　than　6%;　therefore,　a　method　is　needed　to　better　detect　small　crack　targets.　In　this　study,　a　crack　detection　method　based　on　crack　characteristics　and　an　anchor-free　framework　is　investigated.　First,　the　characteristics　of　cracks　are　analyzed　to　obtain　the　real　crack　texture,　interference　noise　texture,　and　targets　appearing　near　each　crack　as　the　context　information　for　the　model　to　filter　and　remove　noise.　We　discuss　the　crack　detection　performance　of　anchor-based　and　anchor-free　algorithms.　Then,　an　optimized　anchor-free　algorithm　is　proposed　in　this　paper　for　crack　detection.　Based　on　the　advantages　of　YOLOX-x,　we　add　a　semantic　enhancement　module　to　better　use　contextual　information.　The　experimental　results　show　that　the　anchor-free　algorithm　performs　slightly　better　than　other　algorithms　in　crack　detection　situations.　In　addition,　the　proposed　method　displays　better　detection　performance　for　slender　and　inconspicuous　cracks,　with　an　average　precision　of　0.858.　©　The　Author(s)　2024.