Semantic　lines　are　some　particular　dominant　lines　in　an　image,　which　divide　the　image　into　several　semantic　regions　and　outline　its　conceptual　structure.　They　play　a　vital　role　in　image　analysis,　scene　understanding,　and　other　downstream　tasks　due　to　their　semantic　representation　ability　for　the　image　layout.　However,　the　accuracy　and　efficiency　of　existing　semantic　line　detection　methods　still　couldn’t　meet　the　need　of　real　applications.　So,　a　new　semantic　line　detection　method　based　on　the　deep　Hough　transform　network　with　attention　mechanism　and　strip　convolution　is　proposed.　Firstly,　the　detection　performance　is　improved　by　combining　the　channel　attention　mechanism　with　the　feature　pyramid　network　to　alleviate　the　influence　of　redundant　information.　Then,　the　strip　convolution　and　mixed　pooling　layer　are　introduced　to　effectively　collect　the　remote　information　and　capture　the　long-range　dependencies　between　pixel　backgrounds.　Finally,　the　strategy　of　GhostNet　is　adopted　to　reduce　the　computational　cost.　Results　of　experiments　on　open　datasets　validate　the　proposed　method,　which　is　comparable　to　and　even　outperforms　the　state-of-the-art　methods　in　accuracy　and　efficiency.　Our　code　and　pretrained　models　are　available　at:　https://github.com/zhizhz/sml.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.